Handbook of Complex Analysis
The Handbook of Complex Analysis will be an entree for advanced undergraduates and beginning graduate students in the subject of complex analysis. The subject of complex analysis of increasing importance. Even the function theory of several complex variables has seen applications in cosmology, geophysics, and engineering.
Applied Statistical Modelling for Ecologists
**2025 PROSE Award Finalist in Environmental Science** Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand. This book is the successor of the widely used Introduction to WinBUGS for Ecologists (K矇ry, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks. The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB.
An Introduction to Scientific Computing with MATLAB(R) and Python Tutorials
This textbook covers essential numerical techniques including basic methods for linear systems, root finding, interpolation, numerical integration and differentiation, least squares and Monte Carlo methods. This book balances the development, implementation, application and analysis of computational ideas and methods.
Introduction to Advanced Engineering Mathematics and Simulation
Introduction to Advanced Engineering Mathematics and Simulation in MATLAB Applications Approach has been prepared for instructors, learners and researchers interested in Advanced Engineering Mathematics and Simulation. The resource has emphasis on practical problems formulation and solving in Engineering. Interest is specifically guiding on developing solutions using the mathematical formulas and codes in MATLAB.
Data Science
Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.
Partial Differential Equations
Designed for a one-semester course, this text provides a bridge between the standard PDE course for undergraduate students in science and engineering and the PDE course for graduate students in mathematics who are pursuing a research career in analysis.
Probability Theory and Statistics with Real World Applications
The idea of the book is to present a text that is useful for both students of quantitative sciences and practitioners who work with univariate or multivariate probabilistic models. Since the text should also be suitable for self-study, excessive formalism is avoided though mathematical rigor is retained. A deeper insight into the topics is provided by detailed examples and illustrations. The book covers the standard content of a course in probability and statistics. However, the second edition includes two new chapters about distribution theory and exploratory data analysis. The first-mentioned chapter certainly goes beyond the standard material. It is presented to reflect the growing practical importance of developing new distributions. The second new chapter studies intensively one- and bidimensional concepts like assymetry, kurtosis, correlation and determination coefficients. In particular, examples are intended to enable the reader to take a critical look at the appropriateness of the geometrically motivated concepts.
Numerical Methods and Analysis with Mathematical Modelling
What sets this book apart is the modeling aspects utilizing numerical analysis (methods) to obtain solutions. The authors cover the basic numerical analysis methods first with simple examples to illustrate the techniques and discuss possible errors.
From Numbers to Knowledge
This book explores the transformative journey of data analysis, beginning with foundational concepts like data collection and cleaning, progressing through exploratory techniques such as descriptive statistics and data visualization, and culminating in advanced methodologies including inferential statistics, predictive modeling, and machine learning. It emphasizes the importance of data quality, effective analysis techniques, and the application of insights to drive informed decision-making across various domains. Through practical examples and case studies, it equips readers with the tools and knowledge to harness the power of data for meaningful impact and innovation.
Fundamentals of Fourier Analysis
This self-contained text introduces Euclidean Fourier Analysis to graduate students who have completed courses in Real Analysis and Complex Variables. It provides sufficient content for a two course sequence in Fourier Analysis or Harmonic Analysis at the graduate level. In true pedagogical spirit, each chapter presents a valuable selection of exercises with targeted hints that will assist the reader in the development of research skills. Proofs are presented with care and attention to detail. Examples are provided to enrich understanding and improve overall comprehension of the material. Carefully drawn illustrations build intuition in the proofs. Appendices contain background material for those that need to review key concepts. Compared with the author's other GTM volumes (Classical Fourier Analysis and Modern Fourier Analysis), this text offers a more classroom-friendly approach as it contains shorter sections, more refined proofs, and a wider range of exercises. Topics include the Fourier Transform, Multipliers, Singular Integrals, Littlewood-Paley Theory, BMO, Hardy Spaces, and Weighted Estimates, and can be easily covered within two semesters.
Geographic Information, Geospatial Technologies and Spatial Data Science for Health
In this book, epidemiology and public health are integrated with spatial data science to examine health issues in dynamically changing environments. This is too broad a field to be covered in one book and so it has been necessary to be selective with the topics, methods and examples.
Modern Statistics with R
The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit.
Real Analysis and Foundations
This new edition continues the effort to make the book accessible to a broader audience. Many students who take a real analysis course do not have the ideal background. The new edition offers chapters on background material like set theory, logic, and methods of proof. The more advanced material in the book is made more apparent.
Observability and Mathematics
The author approaches an old classic problem - the existence of solutions of Navier-Stokes eqs. The main objective is to model and derive of equation of continuity, Euler equation of fluid motion, energy flux equation, Navier-Stokes eqs from the observer point of view and solve classic problem for this interpretation of fluid motion laws.
Numerical Analysis and Scientific Computation
This is an introductory single-term numerical analysis text with a modern scientific computing flavor. It offers an immediate immersion in numerical methods featuring an up-to-date approach to computational matrix algebra and methods used in actual software packages, highlighting how hardware concerns can impact the choice of algorithm.
Bayesian Applications in Environmental and Ecological Studies with R and Stan
The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, which could benefit from the potential power and flexibility of Bayesian methods.
Computational Framework for the Finite Element Method in MATLAB(R) and Python
This book aims to provide a programming framework for coding linear FEM using matrix-based MATLAB language and Python scripting language. It describes FEM algorithm implementation in the most generic formulation so that it is possible to apply this algorithm to as many application problems as possible.
Promoting Statistical Practice and Collaboration in Developing Countries
The book addresses the topics of individual chapters from the perspectives of the historical context, the present state, and future directions of statistical training and practice, so that readers may fully understand the challenges and opportunities in the field of statistics and data science, especially in developing countries.
Multiplicative Differential Calculus
This book is devoted to the multiplicative differential calculus. It summarizes the most recent contributions in this area. The book is intended for senior undergraduate students and beginning graduate students of engineering and science courses.
Optimization Modelling Using R
This book covers using R for doing optimization, a key area of operations research, which has been applied to virtually every industry. The focus is on linear and mixed integer optimization. It uses an algebraic modeling approach for creating formulations that pairs naturally with an algebraic implementation in R.
The Lambert W Function
This book is the very first one in the English language dedicated to the Lambert W function, its generalizations, and its applications. One goal is to promote future research on the topic. The book contains all the information one needs when trying to find a result. The most important formulas and results are framed
Parallel Problem Solving from Nature - Ppsn XVIII
This multi-volume LNCS set, LNCS 15148-15151, constitutes the refereed proceedings of the 18th International Conference on Parallel Problem Solving from Nature, PPSN 2024, held in Hagenberg, Austria, in September 2024. The 101 full papers presented in these proceedings were carefully reviewed and selected from 294 submissions. The papers presented in these four volumes are organized in the following topical sections: Part I: Combinatorial Optimization; Genetic Programming; Fitness Landscape Modeling and Analysis. Part II: Benchmarking and Performance Measures; Automated Algorithm Selection and Configuration; Numerical Optimization; Bayesian- and Surrogate-Assisted Optimization. Part III: Theoretical Aspects of Nature-Inspired Optimization; (Evolutionary) Machine Learning and Neuroevolution; Evolvable Hardware and Evolutionary Robotics. Part IV: Multi-Objective Optimization; Real-World Applications.
Parallel Problem Solving from Nature - Ppsn XVIII
This multi-volume LNCS set, LNCS 15148-15151, constitutes the refereed proceedings of the 18th International Conference on Parallel Problem Solving from Nature, PPSN 2024, held in Hagenberg, Austria, in September 2024. The 101 full papers presented in these proceedings were carefully reviewed and selected from 294 submissions. The papers presented in these four volumes are organized in the following topical sections: Part I: Combinatorial Optimization; Genetic Programming; Fitness Landscape Modeling and Analysis. Part II: Benchmarking and Performance Measures; Automated Algorithm Selection and Configuration; Numerical Optimization; Bayesian- and Surrogate-Assisted Optimization. Part III: Theoretical Aspects of Nature-Inspired Optimization; (Evolutionary) Machine Learning and Neuroevolution; Evolvable Hardware and Evolutionary Robotics. Part IV: Multi-Objective Optimization; Real-World Applications.
Parallel Problem Solving from Nature - Ppsn XVIII
This multi-volume LNCS set, LNCS 15148-15151, constitutes the refereed proceedings of the 18th International Conference on Parallel Problem Solving from Nature, PPSN 2024, held in Hagenberg, Austria, in September 2024. The 101 full papers presented in these proceedings were carefully reviewed and selected from 294 submissions. The papers presented in these four volumes are organized in the following topical sections: Part I: Combinatorial Optimization; Genetic Programming; Fitness Landscape Modeling and Analysis. Part II: Benchmarking and Performance Measures; Automated Algorithm Selection and Configuration; Numerical Optimization; Bayesian- and Surrogate-Assisted Optimization. Part III: Theoretical Aspects of Nature-Inspired Optimization; (Evolutionary) Machine Learning and Neuroevolution; Evolvable Hardware and Evolutionary Robotics. Part IV: Multi-Objective Optimization; Real-World Applications.
Parallel Problem Solving from Nature - Ppsn XVIII
This multi-volume LNCS set, LNCS 15148-15151, constitutes the refereed proceedings of the 18th International Conference on Parallel Problem Solving from Nature, PPSN 2024, held in Hagenberg, Austria, in September 2024. The 101 full papers presented in these proceedings were carefully reviewed and selected from 294 submissions. The papers presented in these four volumes are organized in the following topical sections: Part I: Combinatorial Optimization; Genetic Programming; Fitness Landscape Modeling and Analysis. Part II: Benchmarking and Performance Measures; Automated Algorithm Selection and Configuration; Numerical Optimization; Bayesian- and Surrogate-Assisted Optimization. Part III: Theoretical Aspects of Nature-Inspired Optimization; (Evolutionary) Machine Learning and Neuroevolution; Evolvable Hardware and Evolutionary Robotics. Part IV: Multi-Objective Optimization; Real-World Applications.
Medical Statistics for Cancer Studies
This textbook shows how cancer data can be analysed in a variety of ways, covering cancer clinical trial data, epidemiological data, biological data, and genetic data. It provides detailed overviews of survival analysis, clinical trials, regression analysis, epidemiology, meta-analysis, biomarkers, and cancer informatics.
Real-World Evidence in a Patient-Centric Digital Era
This book will be a vital reference for medical researchers, health technology innovators, data scientists, epidemiologists, population health analysts, health economists, outcomes researchers, as well as policymakers and analysts in the healthcare industry.
Handbook of Fractional Calculus for Engineering and Science
The book includes contributions by top researchers offering topics associated with equations and their relevance and significance in various scientific areas of study and research. The readers will find several important and useful methods and techniques for solving various types of fractional-order models in engineering and science.
The Art and Science of Data Analysis
The Art and Science of Data Analysis provides a thorough guide to the essential principles and techniques of data analysis. It covers key topics such as ensuring data quality, performing exploratory data analysis, understanding statistical foundations, and applying advanced analytical methods like machine learning, clustering, classification, and time series forecasting. The book emphasizes practical applications and hands-on exercises, highlighting the importance of ethical considerations and continuous learning. It aims to equip readers with the knowledge and skills needed for effective data-driven decision-making and innovation in various fields.
Advances in Neural Networks - Isnn 2024
This volume constitutes the refereed proceedings of the 18th International Symposium on Neural Networks, ISNN 2024, held in Weihai, China, during 11-14, July 2024. The 59 full papers were carefully reviewed and selected from 82 submission. They are categorized in the following sections: Optimization Algorithms; Adversarial Learning, Transfer Learning, and Deep Learning; Signal, Image, and Video Processing; Modeling, Analysis, and Implementation of Neural Networks; Control Systems, Robotics, and Autonomous Driving; Fault Diagnosis and Intelligent Industry & Bio-signal, Bioinformatics, and Biomedical Engineering.
Visualizing More Quaternions
Visualizing More Quaternions is a sequel to Dr. Andrew J. Hanson's first book, Visualizing Quaternions, which appeared in 2006. This new volume develops and extends concepts that have attracted the author's attention in the intervening 18 years, providing new insights into existing scholarship, and detailing results from Dr. Hanson's own published and unpublished investigations relating to quaternion applications. Among the topics covered are the introduction of new approaches to depicting quaternions and their properties, applications of quaternion methods to cloud matching, including both orthographic and perspective projection problems, and orientation feature analysis for proteomics and bioinformatics. The quaternion adjugate variables are introduced to embody the nontrivial quaternion topology on the three-sphere and incorporate it into machine learning tasks. Other subjects include quaternion applications to a wide variety of problems in physics, including quantum computing, complexified quaternions in special relativity, and a detailed study of the Kleinian "ADE"discrete groups of the ordinary two-sphere. Quaternion geometry is also incorporated into the isometric embedding of the Eguchi-Hanson gravitational instanton corresponding to the k = 1 Kleinian cyclic group. Visualizing More Quaternions endeavors to explore novel ways of thinking about challenging problems that are relevant to a broad audience involved in a wide variety of scientific disciplines.
Big Data Analytics and Knowledge Discovery
This book constitutes the proceedings of the 26th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2024, which too place in Naples, Italy, during August 26-28, 2024. The 16 full and 20 short papers included in this book were carefully reviewed and selected from 83 submissions. They were organized in topical sections as follows: Modeling and design; entity matching and similarity; classification; machine learning methods and applications; time series; data repositories;optimization; and data quality and applications.
Statistical Design and Analysis of Experiments
Statistical Design and Analysis of Experiments embraces a holistic approach by building a solid foundation of the theoretical aspects followed by easily relatable numerical examples. Examples are first worked out manually and explained step-by-step, after which the statistical software Minitab is demonstrated throughout the textbook. This novel approach makes the textbook applicable to any undergraduate or graduate course in design of experiments (DOE) as well as a valuable resource in the hands of any experimenter.After a clear introduction, concepts of design and analysis of experiments are introduced. Utilizing practical examples, hypothesis testing and the concepts of Type-I errors, Type-II errors, and Power are clearly explained. ANOVA assumptions as well as tests of significance and the concept of point and interval estimates are discussed. Other topics include various types of experimental designs like trial and error or factorial designs, Yates algorithm, replication and reflection of designs, the concept of confounding, and design resolution. Manual calculations as well as software are used to fully analyze experimental data and to draw clear and meaningful conclusions. Response surface methodology and central composite design follow to optimize designs. The book concludes with the Taguchi method and its pitfalls, and the use of nonparametric or distribution-free statistics for when the ANOVA assumptions are not met.Highly approachable and filled with practical examples, Statistical Design and Analysis of Experiments will teach students to conduct and analyze practical experiments, and draw meaningful conclusions based on a sound theoretical and statistical foundation. The textbook assumes no prior knowledge of statistics and does not rely on the use of calculus. A complete instructors' guide with PowerPoint slides and solutions to end-of-the-chapter questions is available for teachers who adopt the book.
An Introduction to Statistical Learning
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Games, Decisions, and Markets
This book critically discusses the historical backgrounds and new developments of the theories of games, decisions, and markets, with many possible applications to social and economic problems. Consisting of three connected parts, the book sheds new light on the role of merchants in the market economy under conditions of risk and uncertainty. Part I begins with the question of why and how John von Neumann and Oskar Morgenstern did joint work in game theory, namely, the theoretical study of strategic interactions among several decision makers. The duel between Sherlock Holmes and Professor Moriarty in Conan Doyle's famous detective story is recalled as a great inducement to Neumann and Morgenstern to invent zero-sum, two-person games. More general non-zero-sum games and associated Nash solutions are then discussed in relation to the generation-gap problem between a young couple and an elderly couple. Part II explores a set of very fundamental problems of individual decision making. Thetwo famous axioms of revealed preference ― Samuelson's weak axiom and Houthakker's axiom ― are skillfully connected and empirically reevaluated by the introduction of certain regularity conditions. The revealed preference approach is then extended from the original commodity space to the dual price space. Such dual treatment in microeconomics is further applied to the theory of cost and production, with the decomposition of the total factor price effect into the substitution and scale effects. Part III turns the reader's attention to the interdependence of several markets. The almost forgotten Hicks-Morishima approach is newly revived with graphical illustrations of traded goods. The well-known Jones-Kemp approach to international trade is boldly expanded into the world of risk and uncertainty. Some striking results in comparative static analysis are derived, with favorable implications for the real world.
An Introduction to Analysis
This widely popular textbook provides a mathematically rigorous introduction to analysis of real­valued functions of one variable. This intuitive, student-friendly text is written in a manner that will help to ease the transition from primarily computational to primarily theoretical maths.
Image Analysis for Vision-Based Skimming
Infestation with invasive aquatic weeds is a major problem being faced while maintaining inland water bodies. Cleaning the surface pollution ideally requires a fully autonomous trash skimmer with capabilities for detecting the presence and finding the physical location of nearby free- floating weed clusters, collection of weed clusters using cutting of matted weeds, if required, moving the skimmer close to the cluster, collecting the cluster, and moving the collected weeds to the bank. Vision enabled trash skimmer consists of a trash skimmer connected to a vision sensor with capability to capture individual video frames and analyze them to extract desired information. Under these conditions, providing visual information of the surroundings to the trash skimmer will make its operations more convenient, accurate and improve its overall performance. This book focuses on improving the performance of the image processing steps namely horizon line detection techniques for segmenting water regions, object detection algorithm for segmenting floating objects on the water surface, color based edge detection algorithm to estimate green-colored weeds in inland water bodies.
Low Biological Energies
As a complement to the four volumes of the naturalist suite, this essay on low biological energies was needed to explore the transitions between quantum mechanics and statistical physics at work in the integration-level units of the living - proteins, the cell, and the animal. From questioning to questioning the coordination of cellular activity, marine historian and naturalist Claude ROUQUETTE embarks on a navigation at sight, to plumb the depths of low energies in order to take stock of the complex processes in biological evolution, intimately associated with the transformations of civilization.
Games, Gambling, and Probability
The goal for this textbook is to complement the inquiry-based learning movement. According to the author, concepts and ideas will stick with the reader more when they are motivated in an interesting way. Topics are presented mathematically as questions about the games themselves are posed.
Economic analysis on a small rural property
Rural properties, regardless of size, need strategies for decision-making. Given this context, this study maps and evaluates three sources of resources on a particular rural property, with the aim of describing business management on small rural properties, emphasizing the need for an ideal evaluation of the viability of economic activities, as a strategic management tool bringing data, statements and results achieved, in order to bring a better perception of the agricultural segment to rural properties and bringing data analyzed through mathematical models that can maximize the profits of the property under study. As such, through this study it can be identified that under the current conditions, dairy production is unviable, compared to the other two activities on the property, providing an optimal model for maximizing profit, which would be the joint production of corn and soybeans, thus maximizing profit.
Identifiability and Observability in Epidemiological Models
An Introduction to Stochastic Integration and It繫 Calculus
This book delves into the comprehensive construction of the It繫 theory concerning stochastic integration and its pivotal role, particularly within the framework of stochastic differential equations. The book meticulously examines the distinctive properties of this integral, along with their respective extensions of the It繫 formula, elucidating their significance in various theoretical and practical contexts. Furthermore, the integration theories are seamlessly integrated into the realm of stochastic differential equations, paving the way for examining complex phenomena such as epidemic models, and ecological models. In this context, the book proposes some original theorems to address pertinent issues about its implications within the framework of stochastic differential equations. Through rigorous construction analysis, this book contributes to the advancement of stochastic calculus theory, offering insights into its application in dynamical population modeling.
