An Investigation of the Effects of Correlation, Autocorrelation, and Sample Size in Classifier Fusion
This thesis extends the research found in Storm, Bauer, and Oxley, 2003. Data correlation effects and sample size effects on three classifier fusion techniques and one data fusion technique were investigated. Identification System Operating Characteristic Fusion (Haspert, 2000), the Receiver Operating Characteristic "Within" Fusion method (Oxley and Bauer, 2002), and a Probabilistic Neural Network were the three classifier fusion techniques; a Generalized Regression Neural Network was the data fusion technique. Correlation was injected into the data set both within a feature set (autocorrelation) and across feature sets for a variety of classification problems, and sample size was varied throughout. Total Probability of Misclassification (TPM) was calculated for some problems to show the effect of correlation on TPM.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Mathematical Programming Model for Fighter Training Squadron Pilot Scheduling
The United States Air Force fighter training squadrons build weekly schedules using a long and tedious process. Very little of this process is automated and optimality of any kind is nearly impossible. Schedules are built to a feasible condition only to be changed with consideration of Wing level requirements. Weekly flying schedules are restricted by requirements for crew rest, days since a pilot's last sortie, sorties in the last 30 days, and sorties in the last 90 days. By providing a scheduling model to the pilot charged with creating the schedule, valuable pilot hours could be spent in the cockpit, simulator, or other required duty. This research effort presents a mathematical programming (MP) approach to the fighter squadron pilot training scheduling problem. The methodology presented is based on binary variables that will provide integer solutions to every feasible set of inputs. A simulator heuristic developed specifically for this problem assigns pilots to simulator sorties based on the feasible solutions obtained from two different formulation and solving approaches. One approach assigns training mission sorties and duties for the entire week, while the other approach breaks the week into ten successive sub-problems. The model constructs two feasible schedules in approximately 2.5 minutes.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Quadrature, Interpolation and Observability
Methods of interpolation and quadrature have been used for over 300 years. Improvements in the techniques have been made by many, most notably by Gauss, whose technique applied to polynomials is referred to as Gaussian Quadrature. Stieltjes extended Gauss's method to certain non-polynomial functions as early as 1884. Conditions that guarantee the existence of quadrature formulas for certain collections of functions were studied by Tchebycheff, and his work was extended by others. Today, a class of functions which satisfies these conditions is called a Tchebycheff System. This thesis contains the definition of a Tchebycheff System, along with the theorems, proofs, and definitions necessary to guarantee the existence of quadrature formulas for such systems. Solutions of discretely observable linear control systems are of particular interest, and observability with respect to a given output function is defined. The output function is written as a linear combination of a collection of orthonormal functions. Orthonormal functions are defined, and their properties are discussed. The technique for evaluating the coefficients in the output function involves evaluating the definite integral of functions which can be shown to form a Tchebycheff system. Therefore, quadrature formulas for these integrals exist, and in many cases are known. The technique given is useful in cases where the method of direct calculation is unstable. The condition number of a matrix is defined and shown to be an indication of the the degree to which perturbations in data affect the accuracy of the solution. In special cases, the number of data points required for direct calculation is the same as the number required by the method presented in this thesis. But the method is shown to require more data points in other cases. A lower bound for the number of data points required is given.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
The Application of Category Theory and Analysis of Receiver Operating Characteristics to Information Fusion
Multisensor data fusion is presented in a rigorous mathematical format, with definitions consistent with the desires of the data fusion community. A model of event-state fusion is developed and described . Definitions of fusion rules and fusors are introduced, along with functor categories, of which they are objects. Defining fusors and competing fusion rules involves the use of an objective function of the researchers choice. One such objective function, a functional on families of classification systems, and in particular receiver operating characteristics (ROCs), is introduced. Its use as an objective function is demonstrated in that the argument which minimizes it (a particular ROC), corresponds to the Bayes Optimal threshold, given certain assumptions, within a family of classification systems. This constraint is extended to ROC manifolds in higher dimensions. Under different data assumptions, the minimizing argument of the ROC functional is shown to be the point of a ROC manifold corresponding to the Neyman-Pearson criteria. A second functional is shown to determine the min-max threshold. A more robust functional is developed.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Statistical Methods for Image Registration and Denoising
This dissertation describes research into image processing techniques that enhance military operational and support activities. The research extends existing work on image registration by introducing a novel method that exploits local correlations to improve the performance of projection-based image registration algorithms. The dissertation also extends the bounds on image registration performance for both projection-based and full-frame image registration algorithms and extends the Barankin bound from the one-dimensional case to the problem of two-dimensional image registration. It is demonstrated that in some instances, the Cramer-Rao lower bound is an overly-optimistic predictor of image registration performance and that under some conditions, the Barankin bound is a better predictor of shift estimator performance. The research also looks at the related problem of single-frame image denoising using block-based methods. The research introduces three algorithms that operate by identifying regions of interest within a noise-corrupted image and then generating noise free estimates of the regions as averages of similar regions in the image.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Traveling Salesman Problem for Surveillance Mission Using Particle Swarm Optimization
The surveillance mission requires aircraft to fly from a starting point through defended terrain to targets and return to a safe destination (usually the starting point). The process of selecting such a flight path is known as the Mission Route Planning (MRP) Problem and is a three-dimensional, multi-criteria (fuel expenditure, time required, risk taken, priority targeting, goals met, etc.) path search. Planning aircraft routes involves an elaborate search through numerous possibilities, which can severely task the resources of the system being used to compute the routes. Operational systems can take up to a day to arrive at a solution due to the combinatoric nature of the problem. This delay is not acceptable because timeliness of obtaining surveillance information is critical in many surveillance missions. Also, the information that the software uses to solve the MRP may become invalid during computation. An effective and efficient way of solving the MRP with multiple aircraft and multiple targets is desired. One approach to funding solutions is to simplify and view the problem as a two-dimensional, minimum path problem.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
An Investigation of the Effects of Correlation, Autocorrelation, and Sample Size in Classifier Fusion
This thesis extends the research found in Storm, Bauer, and Oxley, 2003. Data correlation effects and sample size effects on three classifier fusion techniques and one data fusion technique were investigated. Identification System Operating Characteristic Fusion (Haspert, 2000), the Receiver Operating Characteristic "Within" Fusion method (Oxley and Bauer, 2002), and a Probabilistic Neural Network were the three classifier fusion techniques; a Generalized Regression Neural Network was the data fusion technique. Correlation was injected into the data set both within a feature set (autocorrelation) and across feature sets for a variety of classification problems, and sample size was varied throughout. Total Probability of Misclassification (TPM) was calculated for some problems to show the effect of correlation on TPM.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Updating Optimal Decisions Using Game Theory and Exploring Risk Behavior Through Response Surface Methodology
This thesis utilizes game theory within a framework for updating optimal decisions based on new information as it becomes available. Methodology is developed that allows a decision maker to change his perceived optimal policy based on available knowledge of the opponents strategy, where the opponent is a rational decision maker or a random component nature. Utility theory is applied to account for the different risk preferences of the decision makers. Furthermore, response surface methodology is used to explore good risk strategies for the decision maker to approach each situation with. The techniques are applied to a combat scenario, a football game, and a terrorist resource allocation problem, providing a decision maker with the best possible strategy given the information available to him. The results are intuitive and exemplify the benefits of using the methods.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Cross-Resolution Combat Model Calibration Using Bootstrap Sampling
The US Air Force uses many combat simulation models to assist them in performing combat analyses. BRAWLER is a high-resolution air-to-air combat simulation model used for engagement-level analyses of few-on-few air combat. THUNDER is a low-resolution combat simulation model used for campaign-level analyses of theater-level warfare. BRAWLER is frequently used to ensure that THUNDER air-to-air inputs are valid. This thesis describes the confederation of THUNDER and BRAWLER by clearly showing how one particular BRAWLER output, the effectiveness of a missile type, is transformed into THUNDER air-to-air input data. Since BRAWLER is a stochastic simulation model, it is necessary to replicate a number of BRAWLER simulation runs in order to obtain a sufficiently accurate estimate of the mean missile effectiveness, a number that varies for each different BRAWLER combat scenario. This thesis focuses on using two different sequential methods to determine when the minimum number of BRAWLER runs has been performed to obtain a specified relative precision. One method uses classical statistical analysis techniques, while the other uses the more modern technique of bootstrap resampling. The performance of these two methods is compared.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
The Application of Category Theory and Analysis of Receiver Operating Characteristics to Information Fusion
Multisensor data fusion is presented in a rigorous mathematical format, with definitions consistent with the desires of the data fusion community. A model of event-state fusion is developed and described . Definitions of fusion rules and fusors are introduced, along with functor categories, of which they are objects. Defining fusors and competing fusion rules involves the use of an objective function of the researchers choice. One such objective function, a functional on families of classification systems, and in particular receiver operating characteristics (ROCs), is introduced. Its use as an objective function is demonstrated in that the argument which minimizes it (a particular ROC), corresponds to the Bayes Optimal threshold, given certain assumptions, within a family of classification systems. This constraint is extended to ROC manifolds in higher dimensions. Under different data assumptions, the minimizing argument of the ROC functional is shown to be the point of a ROC manifold corresponding to the Neyman-Pearson criteria. A second functional is shown to determine the min-max threshold. A more robust functional is developed.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
A New Sequential Goodness of Fit Test for the Three-Parameter Gamma Distribution With Known Shape Based on Skewness and Kurtosis
This research presents a sequential goodness of fit test for the three-parameter gamma distribution with a known shape. The test is accomplished by employing the two new tests, sample skewness and sample kurtosis, sequentially as test statistics. Unlike the typical goodness of fits using parameter estimation methods such as MLE (maximum likelihood estimation) and MD (minimum distance estimation). This sequential goodness of fit tests using two test statistics above do not involve a substantial degree of computational complexity. Critical values are obtained using large Monte Carlo simulations, which use 'percentile' function, for shapes of 0.5(0.5)4.0 and sample sizes of 5(5)50. Attained significance levels for all combinations of the two tests between = 0:01(0:01)0:20 are also approximated with Monte Carlo simulations. Extensive power studies are then conducted against two sets of alternatives. One is against common alternative distributions and the other is comparative study whose competitor are popular EDF test statistics such as the Anderson-Darling, Cramer-von Mises, and Komogrov-Smirove tests.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Anderson-Darling and Cramer-Von Mises Based Goodness-of-Fit Tests for the Weibull Distribution With Known Shape Using Normalized Spacings
Two new goodness-of-_t tests are developed for the three-parameter Weibull distribution with known shape parameter. These procedures eliminate the need for estimating the unknown location and scale parameters prior to initiating the tests and are easily adapted for censored data. This is accomplished by employing the Anderson-Darling A2 s and Cram_er-von Mises W2 S statistics based on the normalized spacings of the sample data. Critical values of A2 s and W2 s are obtained for common signi_cance levels by large Monte Carlo simulations for shapes of 0.5(0.5)4.0 and sample sizes of 5(5)40 with up to 40% censoring (type II) from the left and/or right. An extensive Monte Carlo power study is also conducted to compare the two tests with each other and with their prominent competitors. The competitors include another spacings test, Z_, and the modi_ed Kolmogorov-Smirnov (KS), Cram_er- von Mises (W2) and Anderson-Darling (A2) EDF tests. The power results indicate that no one test is superior in all situations. When the alternatives considered are tested against a skewed Weibull null distribution, A2 s and W2 s achieve considerably higher power than the other EDF tests, but do not perform as well as Z_. On the other hand, when the null distribution is symmetric, Z_ loses all of its power, while A2 s and W2 s yield power comparable to the other EDF tests. Results also show A2 s generally outperforms W2 s, and for these reasons, A2 s is the preferred test for the three-parameter Weibull with known shape.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Updating Optimal Decisions Using Game Theory and Exploring Risk Behavior Through Response Surface Methodology
This thesis utilizes game theory within a framework for updating optimal decisions based on new information as it becomes available. Methodology is developed that allows a decision maker to change his perceived optimal policy based on available knowledge of the opponents strategy, where the opponent is a rational decision maker or a random component nature. Utility theory is applied to account for the different risk preferences of the decision makers. Furthermore, response surface methodology is used to explore good risk strategies for the decision maker to approach each situation with. The techniques are applied to a combat scenario, a football game, and a terrorist resource allocation problem, providing a decision maker with the best possible strategy given the information available to him. The results are intuitive and exemplify the benefits of using the methods.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Anderson-Darling and Cramer-Von Mises Based Goodness-of-Fit Tests for the Weibull Distribution With Known Shape Using Normalized Spacings
Two new goodness-of-_t tests are developed for the three-parameter Weibull distribution with known shape parameter. These procedures eliminate the need for estimating the unknown location and scale parameters prior to initiating the tests and are easily adapted for censored data. This is accomplished by employing the Anderson-Darling A2 s and Cram_er-von Mises W2 S statistics based on the normalized spacings of the sample data. Critical values of A2 s and W2 s are obtained for common signi_cance levels by large Monte Carlo simulations for shapes of 0.5(0.5)4.0 and sample sizes of 5(5)40 with up to 40% censoring (type II) from the left and/or right. An extensive Monte Carlo power study is also conducted to compare the two tests with each other and with their prominent competitors. The competitors include another spacings test, Z_, and the modi_ed Kolmogorov-Smirnov (KS), Cram_er- von Mises (W2) and Anderson-Darling (A2) EDF tests. The power results indicate that no one test is superior in all situations. When the alternatives considered are tested against a skewed Weibull null distribution, A2 s and W2 s achieve considerably higher power than the other EDF tests, but do not perform as well as Z_. On the other hand, when the null distribution is symmetric, Z_ loses all of its power, while A2 s and W2 s yield power comparable to the other EDF tests. Results also show A2 s generally outperforms W2 s, and for these reasons, A2 s is the preferred test for the three-parameter Weibull with known shape.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
A New Sequential Goodness of Fit Test for the Three-Parameter Gamma Distribution With Known Shape Based on Skewness and Kurtosis
This research presents a sequential goodness of fit test for the three-parameter gamma distribution with a known shape. The test is accomplished by employing the two new tests, sample skewness and sample kurtosis, sequentially as test statistics. Unlike the typical goodness of fits using parameter estimation methods such as MLE (maximum likelihood estimation) and MD (minimum distance estimation). This sequential goodness of fit tests using two test statistics above do not involve a substantial degree of computational complexity. Critical values are obtained using large Monte Carlo simulations, which use 'percentile' function, for shapes of 0.5(0.5)4.0 and sample sizes of 5(5)50. Attained significance levels for all combinations of the two tests between = 0:01(0:01)0:20 are also approximated with Monte Carlo simulations. Extensive power studies are then conducted against two sets of alternatives. One is against common alternative distributions and the other is comparative study whose competitor are popular EDF test statistics such as the Anderson-Darling, Cramer-von Mises, and Komogrov-Smirove tests.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Statistical Methods for Image Registration and Denoising
This dissertation describes research into image processing techniques that enhance military operational and support activities. The research extends existing work on image registration by introducing a novel method that exploits local correlations to improve the performance of projection-based image registration algorithms. The dissertation also extends the bounds on image registration performance for both projection-based and full-frame image registration algorithms and extends the Barankin bound from the one-dimensional case to the problem of two-dimensional image registration. It is demonstrated that in some instances, the Cramer-Rao lower bound is an overly-optimistic predictor of image registration performance and that under some conditions, the Barankin bound is a better predictor of shift estimator performance. The research also looks at the related problem of single-frame image denoising using block-based methods. The research introduces three algorithms that operate by identifying regions of interest within a noise-corrupted image and then generating noise free estimates of the regions as averages of similar regions in the image.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Boundary Value Problems
This new edition presents an updated and expanded exploration of boundary value problems for fractional dynamic equations on arbitrary time scales, including Caputo fractional dynamic equations, impulsive Caputo fractional dynamic equations, and impulsive Riemann-Liouville fractional dynamic equations. In a new chapter, the author introduces time scale calculus and fractional time scale calculus. The book also covers initial value problems, boundary value problems, initial boundary value problems for each type of equation. The author provides integral representations of the solutions and proves the existence and uniqueness of the solutions. This second edition includes new and updated examples and problems.
Models Demystified
Unlock the Power of Data Science and Machine LearningIn this comprehensive guide, we delve into the world of data science, machinelearning, and AI modeling, providing readers with a robust foundation and practical skills to tackle real-world problems. From basic modeling techniques to advanced machine learning algorithms, this book covers a wide range of topics, ensuring that readers at all levels can benefit from its content. Each chapter is meticulously crafted to offer clear explanations, hands-on examples, and code snippets in both Python and R, making complex concepts accessible and actionable. Additional focus is placed on model interpretation and estimation, common data issues, modeling pitfalls to avoid, and best practices for modeling in general.
Advanced Engineering Mathematics with Matlab(r)
In the five previous editions of Advanced Engineering Mathematics with MATLAB(R), the author presented a text firmly grounded in mathematics that engineers and scientists must understand and know how to use. Tapping into decades of teaching at the US Navy Academy and the US Military Academy and serving for twenty-five years at (NASA) Goddard Space Flight, he combines teaching and practical experience that is rare among authors of advanced engineering mathematics books.This edition continues to refine a smaller, easier to read, and useful version of this classic textbook. While competing textbooks continue to grow, the book presents a slimmer, more practical option to align with the expectations of today's students.The new edition of the author's classic textbook continues on a path to creating the best possible learning resource for instructors and students alike. Through extensive class testing over five previous editions, including the author's current course at the US Naval Academy, the book has been steadily improved.The primary mission of this edition is to dramatically increase the quality and quantity of examples and problems, especially in the chapters on differential equations and Laplace transforms. The chapters on differential equations, linear algebra, Fourier series, and Laplace transforms have seen the greatest changes. Of course, this edition continues to offer a wealth of examples and applications from scientific and engineering literature, a highlight of previous editions.MATLAB(R) remains central to the presentation and is employed to reinforce the concepts that are taught. Worked solutions are given in the back of the book. An Instructor's Solutions Manual is also available.
Health Metrics and the Spread of Infectious Diseases
Health Metrics and the Spread of Infectious Diseases: Machine Learning Applications and Spatial Modelling Analysis with R is an introductory guide to health metrics and infectious diseases. It demonstrates how to calculate these metrics to compare the health status of different countries and explores the world of infectious diseases. It tests various machine learning tools for analyzing trends and relationships among key variables, aiming to prevent unexpected outcomes. Through detailed explanations and practical examples, readers will gain a comprehensive understanding of Disability Adjusted Life Years (DALYs) and their components.Key Features: Structured into four main sections--foundational health metrics, machine learning applications, data visualization, and real-world case studies Integrates real-world case studies with data visualization and machine learning techniques, including spatial modelling with the R programming language Covers specific infectious diseases such as COVID-19 and malaria, providing insights into their spread and control Includes detailed explanations, practical exercises, and clear illustrations to enhance understanding and application Adopts a practical approach, making advanced concepts accessible to a wide audience The book is primarily aimed at researchers, data scientists, and public health professionals who seek to leverage data to improve health outcomes. By blending theoretical knowledge with practical applications, the book equips readers with the tools to make informed decisions and produce meaningful data analyses in public health.
Comparative Analysis of the Fink, Murphy, Bollman and Triangular
"Comparative Analysis of the Fink, Murphy, Bollman and Triangular" presents a rigorous examination of these mathematical models. Authored by C Shaler Smith, this work delves into the theoretical underpinnings and practical applications of each model, offering a detailed comparison of their strengths and weaknesses. This book is an essential resource for researchers, academics, and students seeking a comprehensive understanding of these mathematical concepts.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Journal Of The Royal Statistical Society
This volume, "Volume 29" of the "Journal Of The Royal Statistical Society," presents a compilation of articles and discussions from the society's meetings. The journal serves as a key resource for statisticians, researchers, and anyone interested in the application of statistical methods. It covers a broad range of topics within statistical theory and its practical applications across various disciplines. A valuable historical record of statistical thought, this volume provides insight into the development of statistical practices and their impact on society.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Robust Statistics Through the Monitoring Approach
This open access book presents robust statistical methods and procedures through the monitoring approach, with an emphasis on applications to linear regression. Illustrating the theory, it explores both large and small-sample properties. The performance of the forward search and of the monitoring of static robust estimators for regression data are illuminated through numerous data analyses using MATLAB and R. The book describes the results of many years' work of the authors in the development of powerful methods of robust regression analysis. Robust methods are designed to analyse contaminated data. The well-established static robust methods estimate model features, such as parameter estimates, assuming the amount of contamination in the data is known. These methods are described in detail in Chapter 2 for estimation in a simple sample. The extension to regression is presented in Chapter 3, with an emphasis on S-estimation and related procedures as well as on least trimmed squares. The monitoring methods of Chapter 4, including the forward search, find the appropriate level of robustness for each data set and so avoid biased estimation from the inclusion of outliers and inefficiency due to the deletion of uncontaminated observations. This analysis is followed by examples which illustrate the use of the interactive graphical analyses associated with the authors' FSDA toolbox. Numerical comparisons of the size and power of outlier tests appear in Chapter 5. Later chapters illustrate applications to response transformation in regression and to non-parametric regression. Extensions of the robust multiple regression model include Bayesian, heteroskedastic, time series and compositional regression, together with the clustering of regression models. Finally, several approaches to model selection are investigated and robust analyses of regression data are presented that illustrate the use of the techniques introduced earlier. Exercises are given at the end of each chapter, with solutions at the end of the book. The MATLAB code can be reproduced using MATLAB Online, without the need for a license, or via the language-agnostic Jupyter notebook environment, after installing the MATLAB kernel. Online computer code is available for all examples and exercises, together with a series of YouTube videos. Aimed at professional statisticians and researchers concerned with insightful data analysis, as well as postgraduate students, the book may also serve as a text for a modern interactive robust regression course.
Recent Developments in Spectral and Approximation Theory
Graph Neural Networks: Essentials and Use Cases
This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals--and how they complement advances in generative, agentic, and physical AI--this book is an essential resource for understanding and leveraging their potential. The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation. Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data--complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains. GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged: Graph Convolutional Networks (GCNs): These networks learn from a node's local neighborhood by aggregating information from adjacent nodes, updating the node's representation in the process. Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance. Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data. GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.
Computational Statistics - Predicting the Future from Sample Data
How can we uncover hidden patterns in noisy, complex data? How can we make reliable predictions in an unpredictable world? And how can the combination of statistical theory, data from diverse sources, and increased computing power help us predict the future? In this age of data, and especially big data, computational statistics has become central to scientific discovery and decision-making. Therefore, this book explores the growing role of computational methods in statistical analysis, highlighting how we can transform sample data into meaningful predictions across various disciplines and domains. This volume brings together five chapters, each of which addresses a unique aspect of modern computational statistics and is ideal for statisticians, data scientists, university professors, graduate students, and researchers who seek to deepen their knowledge of computational statistics and its impact on the real world.
New Perspectives on Optimization Algorithms Research
Optimization processes are present in nearly all human activities, aiming to reduce time and expenses, conserve resources, and maximize efficiency. Classical numerical optimization methods were initially employed in more complex or elaborate problems. This class of methods was convenient to use when equations could correctly and completely formulate all the attributes of the problem to be optimized and whose search space for the solution had convexity characteristics and was not multimodal. Expressing a problem completely through equations is often a difficult (if not impossible) task because linguistic values or employing quantities that are not easily dealt with by classical optimization methods can only describe certain limitations and barriers. Irregular and non-convex search spaces also present problems for classical methods, especially those with multiple local minima and maxima, as they can lead to suboptimal solutions. In this context, researchers in optimization have presented new techniques to overcome these barriers, introducing new concepts and methods that enable working with problems that are not well-defined and in multimodal search spaces. Many of these techniques utilize artificial intelligence methods, and it is possible to optimize problems where their limitations are not clearly defined or where their search spaces contain different local maxima and minima. This book is a contribution in this sense. In it, the reader will find two sections. The first, titled "Methodologies", presents the frontier of state-of-the-art optimization techniques and their applications to various problems. The second section, titled "Applications," presents examples of practical applications of modern techniques in optimizing issues, which can be easily adapted to others with similar characteristics. Thus, the reader can use this book to optimize their problems by applying the solutions presented in the chapters and adapting them to their specific issues.
The Doctrine Of Chances, Or, The Theory Of Gaming, Made Easy
The Doctrine of Chances, or, The Theory of Gaming, Made Easy, explores the mathematical principles underlying games of chance and gambling. Written by William Rouse, this treatise provides a detailed examination of probability as it applies to various games, offering insights into the odds and strategies involved. The book delves into the calculations necessary for understanding and predicting outcomes in gaming scenarios, making it accessible to both mathematicians and enthusiasts interested in the theory behind games of chance. Rouse's work is a valuable resource for anyone seeking to understand the mathematical foundations of gambling and probability, offering a historical perspective on the development of these concepts. This edition preserves the original text, providing readers with an authentic view of early mathematical approaches to gaming.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Industrial Mathematics Practically Applied
"Industrial Mathematics Practically Applied" by Paul V. Farnsworth is a comprehensive guide designed for students in manual training, industrial, and technical schools, as well as for home study. This book offers practical instruction and serves as a valuable reference for understanding the application of mathematics in industrial settings. The book covers a range of topics relevant to various trades and industries, providing clear explanations and examples to aid comprehension. Farnsworth's work emphasizes the importance of mathematical principles in real-world scenarios, making it an essential resource for anyone looking to enhance their skills in industrial mathematics. Originally published in the early 20th century, this book remains a valuable resource for those seeking a practical understanding of mathematics in industrial applications.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Computational Methods for Inverse Problems and Applications
Permutation Statistical Methods for Criminology and Criminal Justice
This book takes a unique approach to explaining permutation statistical methods for advanced undergraduate students, graduate students, faculty, researchers, and other professionals interested in the areas of criminology or criminal justice. The book integrates permutation statistical methods with a wide range of classical statistical methods. It opens with a comparison of two models of statistical inference: the classical population model espoused by J. Neyman and E. Pearson and the permutation model first introduced by R.A. Fisher and E.J.G. Pitman. Numerous comparisons of permutation and classical statistical methods are illustrated with examples from criminology and criminal justice and supplemented with a variety of R scripts for ease of computation. The text follows the general outline of an introductory textbook in statistics with chapters on central tendency, variability, one-sample tests, two-sample tests, matched-pairs tests, completely-randomized analysis of variance, randomized-blocks analysis of variance, simple linear regression and correlation, and the analysis of goodness of fit and contingency. Unlike classical statistical methods, permutation statistical methods do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity, depend solely on the observed data, and do not require random sampling, making permutation statistical methods ideal for analyzing criminology and criminal justice databases. Permutation methods are relatively new in that it took modern computing power to make them available to those working in criminology and criminal justice research. The book contains detailed examples of permutation analyses. Each analysis is paired with a conventional analysis; for example, a permutation test of the difference between experimental and control groups is contrasted with Student's two-sample $t$ test. An added feature is the inclusion of multiple historical notes on the origin and development of both parametric and conventional tests and measures. Designed for an audience with a basic statistical background and a strong interest in parametric and non-parametric statistics, the book can easily serve as a textbook for undergraduate and graduate students in criminology, criminal justice, or sociology, as well as serving as a research source for faculty, researchers, and other professionals in the area of criminology. No statistical training beyond a first course in statistics is required, but some knowledge of, or interest in, criminology or criminal justice is assumed.
Some Integral Equations Related to Abel's Equation and the Hilbert Transform
"Some Integral Equations Related to Abel's Equation and the Hilbert Transform" explores advanced mathematical concepts central to the field of integral equations. Authored by Arthur S. Peters, this work delves into the intricate relationships between Abel's equation, the Hilbert transform, and various integral equations. The book offers a rigorous treatment of these topics, providing detailed analysis and methodologies for solving complex problems. It is an invaluable resource for mathematicians, physicists, and engineers seeking a deeper understanding of these mathematical tools and their applications. This enduring work continues to be relevant for researchers and students engaged in mathematical analysis and applied mathematics.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
On the Principle of Limiting Absorptions;
On the Principle of Limiting Absorptions explores advanced topics in mathematical physics and functional analysis. Originally written in Russian and translated by Clyde D. Hill, this work by D.M. Eidus delves into the principle of limiting absorption, a crucial concept in the study of differential operators and spectral theory. This book is an essential resource for researchers and students in mathematics and physics, offering insights into the behavior of solutions to differential equations and their applications in various physical phenomena.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Using the Kth Nearest Neighbor Clustering Procedure to Determine the Number of Subpopulations
This work explores the application of the Kth Nearest Neighbor clustering procedure as a method for determining the number of subpopulations within a dataset. Authored by M. Anthony Wong and Christian Schaak, this study delves into the statistical and computational aspects of using nearest neighbor techniques to identify and delineate distinct groups within complex data. The research provides insights into the practical use of this clustering approach, offering a valuable tool for researchers and practitioners in fields requiring robust data segmentation and analysis. The methods and findings presented can aid in a more accurate understanding of population structures and facilitate informed decision-making based on data-driven insights.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
On Condition Numbers and the Distance to the Nearest Ill-posed Problem
This work explores the relationship between condition numbers and the distance to the nearest ill-posed problem. It provides a rigorous mathematical analysis of condition numbers, particularly in the context of numerical stability and error analysis. The study focuses on understanding how sensitive a problem is to small changes in its input data and investigates the proximity to problems for which solutions are undefined or highly unstable. Intended for researchers and graduate students in mathematics, computer science, and engineering, "On Condition Numbers and the Distance to the Nearest Ill-posed Problem" offers valuable insights into the theoretical foundations of numerical computation and its practical implications. The concepts discussed are crucial for designing robust numerical algorithms and assessing the reliability of computational results.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
On the Formulation and Analysis of Numerical Methods for Time Dependent Transport Equations
This volume, "On the Formulation and Analysis of Numerical Methods for Time Dependent Transport Equations," delves into the intricacies of solving time-dependent transport equations using numerical techniques. It presents a rigorous examination of various methods, offering insights into their formulation and analysis. The work is a valuable resource for researchers and practitioners interested in the mathematical and computational aspects of solving transport phenomena. The book provides a detailed treatment suitable for those seeking a deeper understanding of the subject matter.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Robin Hood Math
How the rich and powerful use math to exploit you, and what you can do to beat them at their own game Everything we do today is recorded as data that's sold to the highest bidder. Plugging our personal data into impersonal algorithms has made government agencies more efficient and tech companies more profitable. But all this comes at a price. It's easy to feel like an insignificant number in a world of number crunchers who care more about their bottom line than your humanity. It's time to flip the equation, turning math into an empowering tool for the rest of us. Award-winning mathematician Noah Giansiracusa explains how the tech giants and financial institutions use formulas to get ahead--and how anyone can use these same formulas in their everyday life. You'll learn how to handle risk rationally, make better investments, take control of your social media, and reclaim agency over the decisions you make each day. In a society that all too often takes from the poor and gives to the rich, math can be a vital democratizing force. Robin Hood Math helps you to think for yourself, act in your own best interests, and thrive.
Some Integral Equations Related to Abel's Equation and the Hilbert Transform
"Some Integral Equations Related to Abel's Equation and the Hilbert Transform" explores advanced mathematical concepts central to the field of integral equations. Authored by Arthur S. Peters, this work delves into the intricate relationships between Abel's equation, the Hilbert transform, and various integral equations. The book offers a rigorous treatment of these topics, providing detailed analysis and methodologies for solving complex problems. It is an invaluable resource for mathematicians, physicists, and engineers seeking a deeper understanding of these mathematical tools and their applications. This enduring work continues to be relevant for researchers and students engaged in mathematical analysis and applied mathematics.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Data Analysis of Medical Studies
This book is written for the many health professionals who are regularly frustrated by elegant but ambiguous descriptions of results of data analysis. It uses articles of the New England Journal of Medicine to demonstrate how ambiguous descriptions of results of data analysis may be read, so that it is clear what they do and do not reveal. These demonstrations also show how statistics is misused.
Applied Engineering Mathematics
The publication of this book is motivated by the need to present the latest research & advancements in the fields of Fluid/Solid Mechanics, Nonlinear Dynamics, and Differential Equations in Applied Mathematics. This book gathers the work of leading experts, offering cutting-edge findings addressing existing challenges in the field. It covers a broad spectrum of topics, including advanced computational methods/mathematical modeling/different Mathematical methods & their applications in many scientific disciplines like predicting the nature and behavior of physical systems in engineering. These problems often require solving differential equations governing fluid flow, heat transfer, and structural deformation, often simultaneously, among other topics. Each chapter delves into specific problems, showcasing interdisciplinary approaches & demonstrating the practical impact of mathematical research on real-world issues. This book is a great resource for scholars, professionals, and researchers as it offers a comprehensive overview of cutting-edge methodologies & innovative solutions. It aims to stimulate additional investigation, promote interdisciplinary collaboration & make substantial contributions to advancing knowledge.
On the Formulation and Analysis of Numerical Methods for Time Dependent Transport Equations
This volume, "On the Formulation and Analysis of Numerical Methods for Time Dependent Transport Equations," delves into the intricacies of solving time-dependent transport equations using numerical techniques. It presents a rigorous examination of various methods, offering insights into their formulation and analysis. The work is a valuable resource for researchers and practitioners interested in the mathematical and computational aspects of solving transport phenomena. The book provides a detailed treatment suitable for those seeking a deeper understanding of the subject matter.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
An Empirical Study of Re-Sampling Techniques as a Method for Improving Error Estimates in Split-Plot Designs
For any acquisition program, whether Department of Defense (DOD) or industry related, the primary driving factor behind the success of a program is whether or not the program remains within budget, stays on schedule and meets the defined performance requirements. If any of these three criteria are not met, the program manager may need to make challenging decisions. Typically, if the program is expected to not stay within budget or is expected to be delayed for one reason or another, the program manager will tend to limit areas of testing in order to meet these criteria. The result tends to be a reduction in the test budget and/or a shortening in the test timeline, both of which are already lean. The T and E community needs new test methodologies to test systems and gain insight on whether a system meets performance standards, within the budget and timeline constraints. In particular, both fundamental and advanced aspects of experimental design need to be adapted. The use of experiential design within DOD has continued to grow because of the needed adaptation. Many different types of experiments have been used. An experimental design that is often needed is one that involves a restricted randomization design such as a split-plot design. Split-plot designs arise when specific factors are difficult (or impossible) to vary, a frequent occurrence within the T and E community.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Using the Kth Nearest Neighbor Clustering Procedure to Determine the Number of Subpopulations
This work explores the application of the Kth Nearest Neighbor clustering procedure as a method for determining the number of subpopulations within a dataset. Authored by M. Anthony Wong and Christian Schaak, this study delves into the statistical and computational aspects of using nearest neighbor techniques to identify and delineate distinct groups within complex data. The research provides insights into the practical use of this clustering approach, offering a valuable tool for researchers and practitioners in fields requiring robust data segmentation and analysis. The methods and findings presented can aid in a more accurate understanding of population structures and facilitate informed decision-making based on data-driven insights.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Jacobi's Method is More Accurate Than QR
This study analyzes the accuracy of Jacobi's method in comparison to the QR algorithm for matrix computations. Originally published in 1988, "Jacobi's Method is More Accurate Than QR" presents a rigorous mathematical treatment demonstrating the circumstances under which Jacobi's method offers superior precision. The research contributes valuable insights to the field of numerical analysis and is useful to researchers and practitioners dealing with eigenvalue problems and matrix diagonalization.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
