Parametric Optimization and Related Topics
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The Arithmetic of Life and Death
Whether you realize it or not, numbers are everywhere--and integral to almost every facet of your life . . . from your next raise in pay to the inevitable rise of inflation, your weekly family budget to your end of the national debt. And as George Shaffner amazingly reveals, there are discerning answers (and a great measure of comfort) in numbers. In The Arithmetic of Life, he applies the basic principles of mathematics--addition, subtraction, multiplication, and division--to some of the most profound and just plain puzzling questions of our time. Illuminated with anecdotes, humor, and insight, each chapter explains a unique part of life that can be understood only through the magic of numbers. Whether it's an unconventional theory on why more things go wrong than right, a simple calculation of how much it will cost you to smoke for a lifetime, why crime (accumulatively) doesn't pay, or a glimpse into the probability of life after death, this enlightening and lucidly reasoned book will forever change the way you think about numbers--and the world around you.
Objective Bayesian Inference
Bayesian analysis is today understood to be an extremely powerful method of statistical analysis, as well an approach to statistics that is particularly transparent and intuitive. It is thus being extensively and increasingly utilized in virtually every area of science and society that involves analysis of data.A widespread misconception is that Bayesian analysis is a more subjective theory of statistical inference than what is now called classical statistics. This is true neither historically nor in practice. Indeed, objective Bayesian analysis dominated the statistical landscape from roughly 1780 to 1930, long before 'classical' statistics or subjective Bayesian analysis were developed. It has been a subject of intense interest to a multitude of statisticians, mathematicians, philosophers, and scientists. The book, while primarily focusing on the latest and most prominent objective Bayesian methodology, does present much of this fascinating history.The book is written for four different audiences. First, it provides an introduction to objective Bayesian inference for non-statisticians; no previous exposure to Bayesian analysis is needed. Second, the book provides an overview of the development and current state of objective Bayesian analysis and its relationship to other statistical approaches, for those with interest in the philosophy of learning from data. Third, the book presents a careful development of the particular objective Bayesian approach that we recommend, the reference prior approach. Finally, the book presents as much practical objective Bayesian methodology as possible for statisticians and scientists primarily interested in practical applications.
Complex Heterogeneous Systems
The author's research on energy storage systems generally was confronted with five characteristics, i.e., complex, interacting, transporting, reacting, and heterogeneous systems. Hence, we refer to these kind of systems as Complex Heterogeneous Systems (CHeSs). The work considers interacting systems that exchange energy, mass, information, etc. in various ways. The elementary building blocks of CHeSs are based on fundamental thermodynamic, chemical, material, physical, and mathematical principles such as variational and graph-theoretic concepts. It investigates ways of defining complexity, computing percolation thresholds, making smart decisions also by learning from data/past experiences (e.g., providing a systematic approach towards battery management systems), and identifying battery life (e.g., by blow-up analysis of highly nonlinear concentrated solutions). Ultimately, the elaborated tools shall allow the reader to obtain a general understanding for simulating (also on quantum computers), controlling, and developing CHeSs as well as to pave the way for a general theory on CHeSs generalizing the view on complexity, measurement, estimation, and control.
Comparative Genomics
This book constitutes the proceedings of the 21st International Conference on Comparative Genomics, RECOMB-CG 2024, which was held in Boston, MA, USA, during April 27-28, 2024. The 13 full papers presented in this book were carefully reviewed and selected from 21 submissions. The papers are divided into the following topical sections: phylogenetic networks; homology and phylogenetic reconstruction; tools for evolution reconstruction; genome rearrangements; and genome evolution.
So You Think You Have Brains?
Really Challenging Mathematical Puzzles Newspapers and similar media often feature crossword puzzles, word-searches, general knowledge quizzes, problems on word games like Scrabble and other puzzles, typically on 'brain' games like chess and bridge. As one who has always been fascinated by mathematics, the author offers testing examples from the world of numbers which will require a fair amount of brain-work if you are to find correct solutions. The world of numbers displays much variety and some strange traits. That world forms the first of our group of problems and then the book focuses attention on the three "p's" palindromes, primes, and powers. Later, geometry problems and examples where numbers can be substituted for letters conclude the book. We hope readers enjoy being challenged!
The Art of Finding Hidden Risks
This text gives a comprehensive, largely self-contained treatment of multivariate heavy tail analysis. Emphasizing regular variation of measures means theory can be presented systematically and without regard to dimension. Tools are developed that allow a flexible definition of "extreme" in higher dimensions and permit different heavy tails to coexist on the same state space leading to "hidden regular variation" and "steroidal regular variation". This emphasizes when estimating risks, it is important to choose the appropriate heavy tail. Theoretical foundations lead naturally to statistical techniques; examples are drawn from risk estimation, finance, climatology and network analysis. Treatments target a broad audience in insurance, finance, data analysis, network science and probability modeling. The prerequisites are modest knowledge of analysis and familiarity with the definition of a measure; regular variation of functions is reviewed but is not a focal point.
Upper Bounds for Grothendieck Constants, Quantum Correlation Matrices and CCP Functions
This book concentrates on the famous Grothendieck inequality and the continued search for the still unknown best possible value of the real and complex Grothendieck constant (an open problem since 1953). It describes in detail the state of the art in research on this fundamental inequality, including Krivine's recent contributions, and sheds light on related questions in mathematics, physics and computer science, particularly with respect to the foundations of quantum theory and quantum information theory. Unifying the real and complex cases as much as possible, the monograph introduces the reader to a rich collection of results in functional analysis and probability. In particular, it includes a detailed, self-contained analysis of the multivariate distribution of complex Gaussian random vectors. The notion of Completely Correlation Preserving (CCP) functions plays a particularly important role in the exposition.The prerequisites are a basic knowledge of standard functional analysis, complex analysis, probability, optimisation and some number theory and combinatorics. However, readers missing some background will be able to consult the generous bibliography, which contains numerous references to useful textbooks. The book will be of interest to PhD students and researchers in functional analysis, complex analysis, probability, optimisation, number theory and combinatorics, in physics (particularly in relation to the foundations of quantum mechanics) and in computer science (quantum information and complexity theory).
Extreme Values in Random Sequences
The main subject is the probabilistic extreme value theory. The purpose is to present recent results related to limiting distributions of maxima in incomplete samples from stationary sequences, and results related to extremal properties of different combinatorial configurations. The necessary contents related to regularly varying functions and basic results of extreme value theory are included in the first two chapters with examples, exercises and supplements. The motivation for consideration maxima in incomplete samples arises from the fact that real data are often incomplete. A sequence of observed random variables from a stationary sequence is also stationary only in very special cases. Hence, the results provided in the third chapter are also related to non-stationary sequences. The proof of theorems related to joint limiting distribution of maxima in complete and incomplete samples requires a non-trivial combination of combinatorics and point process theory. Chapter four provides results on the asymptotic behavior of the extremal characteristics of random permutations, the coupon collector's problem, the polynomial scheme, random trees and random forests, random partitions of finite sets, and the geometric properties of samples of random vectors. The topics presented here provide insight into the natural connections between probability theory and algebra, combinatorics, graph theory and combinatorial geometry. The contents of the book may be useful for graduate students and researchers who are interested in probabilistic extreme value theory and its applications.
Elements of Stochastic Modelling (Third Edition)
This is a thoroughly revised and expanded third edition of a successful university textbook that provides a broad introduction to key areas of stochastic modelling. The previous edition was developed from lecture notes for two one-semester courses for third-year science and actuarial students at the University of Melbourne.This book reviews the basics of probability theory and presents topics on Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. It also features elements of stochastic calculus and introductory mathematical finance. This makes the book suitable for a larger variety of university courses presenting the fundamentals of modern stochastic modelling.To make the text covering a lot of material more appealing and accessible to the reader, instead of rigorous proofs we often give only sketches of the arguments, with indications as to why a particular result holds and also how it is related to other results, and illustrate them by examples. It is in this aspect that the present, third edition differs from the second one: the included background material and argument sketches have been extended, made more graphical and informative. The whole text was reviewed and streamlined wherever possible to make the book more attractive and useful for readers. Where appropriate, the book includes references to more specialised texts on respective topics that contain both complete proofs and more advanced material.
Elements of Stochastic Modelling
This is a thoroughly revised and expanded third edition of a successful university textbook that provides a broad introduction to key areas of stochastic modelling. The previous edition was developed from lecture notes for two one-semester courses for third-year science and actuarial students at the University of Melbourne.This book reviews the basics of probability theory and presents topics on Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. It also features elements of stochastic calculus and introductory mathematical finance. This makes the book suitable for a larger variety of university courses presenting the fundamentals of modern stochastic modelling.To make the text covering a lot of material more appealing and accessible to the reader, instead of rigorous proofs we often give only sketches of the arguments, with indications as to why a particular result holds and also how it is related to other results, and illustrate them by examples. It is in this aspect that the present, third edition differs from the second one: the included background material and argument sketches have been extended, made more graphical and informative. The whole text was reviewed and streamlined wherever possible to make the book more attractive and useful for readers. Where appropriate, the book includes references to more specialised texts on respective topics that contain both complete proofs and more advanced material.
Notes on Tug-Of-War Games and the P-Laplace Equation
This book addresses the interplay between stochastic processes and partial differential equations. More specifically, it focuses on the connection between the nonlinear p-Laplace equation and the stochastic game called tug-of-war with noise. The connection in this context was discovered approximately 15 years ago and has since provided new insights and approaches. These lecture notes provide a brief but detailed and accessible introduction to the subject and to the more research-oriented literature. The book also presents the parabolic case side by side with the elliptic case, highlighting the fact that elliptic and parabolic equations are close in spirit in certain aspects. Moreover, it covers some parts of the regularity theory for these problems. Graduate students and advanced undergraduate students with a basic understanding of probability and partial differential equations will find this book useful.
Industrial Engineering and Industrial Management
This CCIS post conference volume constitutes the proceedings of the 5th International Conference, IEIM 2024, in Nice, France, in January 2024. The 18 full papers together with 3 short papers in this volume were carefully reviewed and selected from 71 submissions. The were organized in 5 tracks as follows: five topics of IEIM were classified as follows: "Data Analysis and Demand Calculation in Industrial Production", "Process Optimization and Intelligence in Green Manufacturing Systems", "Lean Manufacturing and Process Optimization", "Enterprise Digital Transformation and Business Management" and "Modern Logistics Information Systems and Distribution Services".
Introduction to Statistics and Data Analysis
Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference.The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.
Mathematics Beyond the Calculus
This text is about solving various types of equations using practical mathematical methods. Only the essentials of each topic are discussed. This is not about proving theorems, taking limits, or other matters important to mathematicians. "However, the emphasis should be somewhat more on how to do the mathematics quickly and easily, and what formulas are true, rather than the mathematicians' interest in methods of rigorous proof." Richard Feynman Concepts from Linear Algebra - the determinant, the finite matrix, the eigenvalue - are presented without the distractions of mathematical rigor. You learn solution methods that do not involve guesses. Methods you implement in a straightforward manner. The operational calculus can be traced back to Oliver Heaviside. Though many scientists preceded Heaviside in introducing operational methods, the systematic use of operational methods in physical problems was stimulated only by Heaviside's work. The methods he created are undoubtedly among the most important ever created. Heaviside was criticized for his lack of mathematical rigor. Yet his numerous mathematical and physical methods and results proved to be correct when mathematical rigor was incorporated. The Laplace Transform, a basis for a modern day operational calculus, is a straightforward technique for solving ordinary, partial differential, and, with a few complications, difference equations and a type of integral equation. On the other hand the Z transform solves difference equations without complications. And, Heaviside's differential operator D = d/dt augments the transform methods. The Laplace Transform transforms equations in one real variable domain, usually the time t domain, to a complex variable domain where the problem at hand is essentially solved. The inverse transform from the complex variable domain to the real variable domain completes the solution. Understanding the inverse transform requires knowledge of the theory of functions of complex variables. Our main interest in functions of a complex variable is integration, because integration of the complicated integrals of inverse transforms is amazingly simplified. The methods of the differential and integral calculus are extended to complex numbers and functions of complex variables. The results produce tremendous analytic methods. We show how ordinary differential equations. systems of ordinary differential equations, partial differential equations, and difference equations are readily solved by transform and/or differential operational methods. We show that each type of equation is solved in essentially the same way. We just define the Fourier Series, and show how to create Fourier series representing waveforms. Integral Equations - This is a hugh subject, which we limit to how the Laplace transform solves integral equations that include the convolution integral. Galois Finite Fields GF(2m) are not used to solve equations per se. They are used to implement functions such as error correcting codes, speech recognition, phase array antennas, and Doppler radar. Functions NOT implemented here.
A Modern Approach to Teaching an Introduction to Optimization
Optimization should be the science of making the best possible decisions. Making decisions is a virtually universal human activity encountered by professionals (in any field) or people in their everyday lives. You would think, then, that the study of making good decisions is a subject that should be taught broadly to students throughout engineering, the physical and social sciences, business, and policy. Yet today, "optimization" is widely taught as a mathematically sophisticated subject, often limited to graduate students in specialized fields. In operations research (or industrial engineering), "optimization" is equivalent to deterministic math programming, starting with linear programs (and the simplex algorithm), and then transitioning through integer linear programs and nonlinear programs. If you are in departments like electrical or mechanical engineering, optimization means teaching optimal control. And if you are in computer science, optimization today could be interpreted in the context of machine learning (such as fitting models to data) or as reinforcement learning. This book claims that the traditional style of teaching optimization is misguided and out of date. First, while the simplex algorithm is a powerful strategy for solving linear programs, the details of the simplex algorithm are completely inappropriate in an introductory course in optimization. Second, while linear programs are appropriate for solving many problems, they are only applicable to a tiny fraction of all decisions. Third, linear programs (along with integer and nonlinear programs) are static models for problems with (typically) vector-valued decisions. By contrast, most decisions are sequential since they are made periodically over time as new information is arriving. In addition, the vast majority of these decisions are scalar (possibly continuous or discrete). This book is designed for instructors (or potential instructors) looking to introduce the science of making good decisions to the broadest possible audience. It should also be of interest to anyone who has already had a traditional course in optimization of any type. The presentation is organized around a series of topics that suggest a fundamentally different approach to teaching "optimization" spanning both sequential decision problems (which offer the simplest problem settings) before transitioning to more complex vector-valued decisions. It also makes the case that most problems which are modeled as linear (or integer, or nonlinear programs) are actually methods for making decisions in a sequential setting. For this reason, these topics are introduced with much less emphasis on algorithms than is traditionally used, both in static and sequential settings.
Bayesian Analysis with Python - Third Edition
Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey Features- Conduct Bayesian data analysis with step-by-step guidance- Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling- Enhance your learning with best practices through sample problems and practice exercises- Purchase of the print or Kindle book includes a free PDF eBook.Book DescriptionThe third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.What you will learn- Build probabilistic models using PyMC and Bambi- Analyze and interpret probabilistic models with ArviZ- Acquire the skills to sanity-check models and modify them if necessary- Build better models with prior and posterior predictive checks- Learn the advantages and caveats of hierarchical models- Compare models and choose between alternative ones- Interpret results and apply your knowledge to real-world problems- Explore common models from a unified probabilistic perspective- Apply the Bayesian framework's flexibility for probabilistic thinkingWho this book is forIf you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.Table of Contents- Thinking Probabilistically- Programming Probabilistically- Hierarchical Models- Modeling with Lines- Comparing Models- Modeling with Bambi- Mixture Models- Gaussian Processes- Bayesian Additive Regression Trees- Inference Engines- Where to Go Next
Getting Started in Mathematical Life Sciences
This book helps the reader make use of the mathematical models of biological phenomena starting from the basics of programming and computer simulation. Computer simulations based on a mathematical model enable us to find a novel biological mechanism and predict an unknown biological phenomenon. Mathematical biology could further expand the progress of modern life sciences. Although many biologists are interested in mathematical biology, they do not have experience in mathematics and computer science. An educational course that combines biology, mathematics, and computer science is very rare to date. Published books for mathematical biology usually explain the theories of established mathematical models, but they do not provide a practical explanation for how to solve the differential equations included in the models, or to establish such a model that fits with a phenomenon of interest. MATLAB is an ideal programming platform for the beginners of computer science. This book starts from the very basics about how to write a programming code for MATLAB (or Octave), explains how to solve ordinary and partial differential equations, and how to apply mathematical models to various biological phenomena such as diabetes, infectious diseases, and heartbeats. Some of them are original models, newly developed for this book. Because MATLAB codes are embedded and explained throughout the book, it will be easy to catch up with the text. In the final chapter, the book focuses on the mathematical model of the proneural wave, a phenomenon that guarantees the sequential differentiation of neurons in the brain. This model was published as a paper from the author's lab (Sato et al., PNAS 113, E5153, 2016), and was intensively explained in the book chapter "Notch Signaling in Embryology and Cancer", published by Springer in 2020. This book provides the reader who has a biological background with invaluable opportunities to learn and practice mathematical biology.
Functional Analysis
This textbook presents the principles of functional analysis in a clear and concise way. The first three chapters describe the general notions of distance, integral, and norm, as well as their relations. Fundamental examples are provided in the three chapters that follow: Lebesgue spaces, dual spaces, and Sobolev spaces. Two subsequent chapters develop applications to capacity theory and elliptic problems. In particular, the isoperimetric inequality and the P籀lya-Szegő and Faber-Krahn inequalities are proved by purely functional methods. The epilogue contains a sketch of the history of functional analysis in relation to integration and differentiation. Starting from elementary analysis and introducing relevant research, this work is an excellent resource for students in mathematics and applied mathematics. The second edition of Functional Analysis includes several improvements as well as the addition of supplementary material. Specifically, the coverage of advanced calculus and distribution theory has been completely rewritten and expanded. New proofs, theorems, and applications have been added as well for readers to explore.
Introducing Game Theory and Its Applications
Introducing Game Theory and its Applications presents an easy-to-read introduction to the basic ideas and techniques of game theory.
Student Solutions Manual for Non Linear Dynamics and Chaos
This official Student Solutions Manual includes solutions to the odd-numbered exercises featured in the third edition of Steven Strogatz's classic text Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering.
Divine Games
A game-theoretical analysis of interactions between a human being and an omnipotent and omniscient godlike being highlights the inherent unknowability of the latter's superiority. In Divine Games, Steven Brams analyzes games that a human being might play with an omnipotent and omniscient godlike being. Drawing on game theory and his own theory of moves, Brams combines the analysis of thorny theological questions, suggested by Pascal's wager (which considers the rewards and penalties associated with belief or nonbelief in God) and Newcomb's problem (in which a godlike being has near omniscience) with the analysis of several stories from the Hebrew Bible. Almost all of these stories involve conflict between God or a surrogate and a human player; their representation as games raises fundamental questions about God's superiority. In some games God appears vulnerable (after Adam and Eve eat the forbidden fruit in defiance of His command), in other games his actions seem morally dubious (when He subjects Abraham and Job to extreme tests of their faith), and in still other games He has a propensity to hold grudges (in preventing Moses from entering the Promised Land and in undermining the kingship of Saul). If the behavior of a superior being is indistinguishable from that of an ordinary human being, his existence would appear undecidable, or inherently unknowable. Consequently, Brams argues that keeping an open mind about the existence of a superior being is an appropriate theological stance.
Discrete Choice Analysis with R
This book is designed as a gentle introduction to the fascinating field of choice modeling and its practical implementation using the R language. Discrete choice analysis is a family of methods useful to study individual decision-making. With strong theoretical foundations in consumer behavior, discrete choice models are used in the analysis of health policy, transportation systems, marketing, economics, public policy, political science, urban planning, and criminology, to mention just a few fields of application. The book does not assume prior knowledge of discrete choice analysis or R, but instead strives to introduce both in an intuitive way, starting from simple concepts and progressing to more sophisticated ideas. Loaded with a wealth of examples and code, the book covers the fundamentals of data and analysis in a progressive way. Readers begin with simple data operations and the underlying theory of choice analysis and conclude by working with sophisticated models including latent class logit models, mixed logit models, and ordinal logit models with taste heterogeneity. Data visualization is emphasized to explore both the input data as well as the results of models. This book should be of interest to graduate students, faculty, and researchers conducting empirical work using individual level choice data who are approaching the field of discrete choice analysis for the first time. In addition, it should interest more advanced modelers wishing to learn about the potential of R for discrete choice analysis. By embedding the treatment of choice modeling within the R ecosystem, readers benefit from learning about the larger R family of packages for data exploration, analysis, and visualization.
Random Graphs and Complex Networks: Volume 2
Complex networks are key to describing the connected nature of the society that we live in. This book, the second of two volumes, describes the local structure of random graph models for real-world networks and determines when these models have a giant component and when they are small-, and ultra-small, worlds. This is the first book to cover the theory and implications of local convergence, a crucial technique in the analysis of sparse random graphs. Suitable as a resource for researchers and PhD-level courses, it uses examples of real-world networks, such as the Internet and citation networks, as motivation for the models that are discussed, and includes exercises at the end of each chapter to develop intuition. The book closes with an extensive discussion of related models and problems that demonstratemodern approaches to network theory, such as community structure and directed models.
An Introduction to Partial Differential Equations with MATLAB
The first two editions of An Introduction to Partial Differential Equations with MATLAB(R) gained popularity among instructors and students at various universities throughout the world. Plain mathematical language is used in a friendly manner to provide a basic introduction to partial differential equations (PDEs).Suitable for a one- or two-semester introduction to PDEs and Fourier series, the book strives to provide physical, mathematical, and historical motivation for each topic. Equations are studied based on method of solution, rather than on type of equation.This third edition of this popular textbook updates the structure of the book by increasing the role of the computational portion, compared to previous editions. The redesigned content will be extremely useful for students of mathematics, physics, and engineering who would like to focus on the practical aspects of the study of PDEs, without sacrificing mathematical rigor. The authors have maintained flexibility in the order of topics.In addition, students will be able to use what they have learned in some later courses (for example, courses in numerical analysis, optimization, and PDE-based programming). Included in this new edition is a substantial amount of material on reviewing computational methods for solving ODEs (symbolically and numerically), visualizing solutions of PDEs, using MATLAB(R)'s symbolic programming toolbox, and applying various schemes from numerical analysis, along with suggestions for topics of course projects.Students will use sample MATLAB(R) or Python codes available online for their practical experiments and for completing computational lab assignments and course projects.
A Comprehensive Guide to Coding and Programming in Stata
This book is an introductory guide to programming and coding in Stata. Commonly encountered code in the field of medical statistics as well as the analyses of observational data are presented.This book covers loops and macros and then describes other commonly required coding commands.
Recent Trends and Future Challenges in Learning from Data
This book collects together selected peer-reviewed contributions presented at the European Conference on Data Analysis, ECDA 2022, held in Naples, Italy, September 14-16, 2022. Highlighting the role of statistics in discovering novel and interesting patterns in the era of big data, it follows the motto of the conference: "Avoiding drowning in the data: recent trends and future challenges in learning from data". The central focus is on multidisciplinary approaches to data analysis, classification, and the interface between computer science, data mining and statistics. Both methodological and applied topics are covered. The former includes supervised and unsupervised techniques with particular emphasis on advances in regression and clustering analysis and constructing composite indicators. The applications are mainly in risk analysis, biology, and education. The volume is organized into four main macro themes: methodological contributions in the social sciences and education, multivariate analysis methods for big data, innovative contributions for applications inspired by biology, and strategies for analyzing complex data in finance.
Modeling with Stochastic Programming
This is an updated version of what is still the only text to address basic questions about how to model uncertainty in mathematical programming, including how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This second edition has important extensions regarding how to represent random phenomena in the models (also called scenario generation) as well as a new chapter on multi-stage models. This text would be suitable as a stand-alone or supplement for a second course in OR/MS or in optimization-oriented engineering disciplines where the instructor wants to explain where models come from and what the fundamental modeling issues are. The book is easy-to-read, highly illustrated with lots of examples and discussions. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty. Alan King is a Research Staff Member at IBM's Thomas J. Watson Research Center in New York. Stein W. Wallace is a Professor of Operational Research and head of Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway.
Fundamentals of Real and Complex Analysis
The primary aim of this text is to help transition undergraduates to study graduate level mathematics. It unites real and complex analysis after developing the basic techniques and aims at a larger readership than that of similar textbooks that have been published, as fewer mathematical requisites are required. The idea is to present analysis as a whole and emphasize the strong connections between various branches of the field. Ample examples and exercises reinforce concepts, and a helpful bibliography guides those wishing to delve deeper into particular topics. Graduate students who are studying for their qualifying exams in analysis will find use in this text, as well as those looking to advance their mathematical studies or who are moving on to explore another quantitative science.Chapter 1 contains many tools for higher mathematics; its content is easily accessible, though not elementary. Chapter 2 focuses on topics in real analysis such as p-adic completion, Banach Contraction Mapping Theorem and its applications, Fourier series, Lebesgue measure and integration. One of this chapter's unique features is its treatment of functional equations. Chapter 3 covers the essential topics in complex analysis: it begins with a geometric introduction to the complex plane, then covers holomorphic functions, complex power series, conformal mappings, and the Riemann mapping theorem. In conjunction with the Bieberbach conjecture, the power and applications of Cauchy's theorem through the integral formula and residue theorem are presented.
The Mathematics of Machine Learning
This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.
An Introduction to Partial Differential Equations
This textbook is an introduction to the methods needed to solve partial differential equations (PDEs). Readers are introduced to PDEs that come from a variety of fields in engineering and the natural sciences. The chapters include the following topics: First Order PDEs, Second Order PDEs, Fourier Series, Separation of Variables, the Fourier Transform, and higher dimensional problems. Readers are guided through these chapters where techniques for solving first and second order PDEs are introduced. Each chapter ends with series of exercises to facilitate learning as well as illustrate the material presented in each chapter.
Synthetic Aperture Radar (Sar) Data Applications
This carefully curated volume presents an in-depth, state-of-the-art discussion on many applications of Synthetic Aperture Radar (SAR). Integrating interdisciplinary sciences, the book features novel ideas, quantitative methods, and research results, promising to advance computational practices and technologies within the academic and industrial communities. SAR applications employ diverse and often complex computational methods rooted in machine learning, estimation, statistical learning, inversion models, and empirical models. Current and emerging applications of SAR data for earth observation, object detection and recognition, change detection, navigation, and interference mitigation are highlighted. Cutting edge methods, with particular emphasis on machine learning, are included. Contemporary deep learning models in object detection and recognition in SAR imagery with corresponding feature extraction and training schemes are considered. State-of-the-art neural network architectures in SAR-aided navigation are compared and discussed further. Advanced empirical and machine learning models in retrieving land and ocean information -- wind, wave, soil conditions, among others, are also included.
Analysis of Categorical Data with R
Analysis of Categorical Data with R, Second Edition presents a modern account of categorical data analysis using the R software environment. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them.The second edition is a substantial update of the first based on the authors' experiences of teaching from the book for nearly a decade. The book is organized as before, but with new content throughout, and there are two new substantive topics in the advanced topics chapter--group testing and splines. The computing has been completely updated, with the "emmeans" package now integrated into the book. The examples have also been updated, notably to include new examples based on COVID-19, and there are more than 90 new exercises in the book. The solutions manual and teaching videos have also been updated.Features: Requires no prior experience with R, and offers an introduction to the essential features and functions of R Includes numerous examples from medicine, psychology, sports, ecology, and many other areas Integrates extensive R code and output Graphically demonstrates many of the features and properties of various analysis methods Offers a substantial number of exercises in all chapters, enabling use as a course text or for self-study Supplemented by a website with data sets, code, and teaching videos Analysis of Categorical Data with R, Second Edition is primarily designed for a course on categorical data analysis taught at the advanced undergraduate or graduate level. Such a course could be taught in a statistics or biostatistics department, or within mathematics, psychology, social science, ecology, or another quantitative discipline. It could also be used by a self-learner and would make an ideal reference for a researcher from any discipline where categorical data arise.
Elliptic Equations: An Introductory Course
The aim of this book is to introduce the reader to different topics of the theory of elliptic partial differential equations by avoiding technicalities and complicated refinements. Apart from the basic theory of equations in divergence form, it includes subjects as singular perturbations, homogenization, computations, asymptotic behavior of problems in cylinders, elliptic systems, nonlinear problems, regularity theory, Navier-Stokes systems, p-Laplace type operators, large solutions, and mountain pass techniques. Just a minimum on Sobolev spaces has been introduced and work on integration on the boundary has been carefully avoided to keep the reader attention focused on the beauty and variety of these issues. The chapters are relatively independent of each other and can be read or taught separately. Numerous results presented here are original, and have not been published elsewhere. The book will be of interest to graduate students and researchers specializing in partial differential equations.
Linear and Nonlinear Non-Fredholm Operators
This book is devoted to a new aspect of linear and nonlinear non-Fredholm operators and its applications. The domain of applications of theory developed here is potentially much wider than that presented in the book. Therefore, a goal of this book is to invite readers to make contributions to this fascinating area of mathematics.First, it is worth noting that linear Fredholm operators, one of the most important classes of linear maps in mathematics, were introduced around 1900 in the study of integral operators. These linear Fredholm operators between Banach spaces share, in some sense, many properties with linear maps between finite dimensional spaces. Since the end of the previous century there has been renewed interest in linear - nonlinear Fredholm maps from a topological degree point of view and its applications, following a period of "stagnation" in the mid-1960s. Now, linear and nonlinear Fredholm operator theory and the solvability of corresponding equations both from the analytical and topological points of view are quite well understood.Also noteworthy is, that as a by-product of our results, we have obtained an important tool for modelers working in mathematical biology and mathematical medicine, namely, the necessary conditions for preserving positive cones for systems of equations without Fredholm property containing local - nonlocal diffusion as well as terms for transport and nonlinear interactions.
An Introduction to Traffic Flow Theory
This second edition of An Introduction to Traffic Flow Theory adds new material in several chapters related to advanced technologies including autonomy, the use of sensors and communications, and particularly congestion mitigation solutions that leverage connected and autonomous vehicles (CAVs). It also includes a new chapter that briefly outlines several mathematical analysis techniques commonly used in traffic flow theory, aiming to introduce students to some of the most frequently used tools available for traffic operational-related analysis. This new edition also includes several updates related to the most recent versions of the Highway Capacity Manual and the Green Book. This textbook is meant for use in advanced undergraduate/graduate level courses in traffic flow theory with prerequisites including two semesters of calculus, statistics, and an introductory course in transportation. The text would also be of interest to transportation professionals as a refresherin traffic flow theory or as a reference. Students and engineers of diverse backgrounds will find this text accessible and applicable to today's traffic issues.This text provides a comprehensive and concise treatment of the topic of traffic flow theory and includes several topics relevant to today's highway transportation system. It provides the fundamental principles of traffic flow theory as well as applications of those principles for evaluating specific types of facilities (freeways, intersections, etc.). Newer concepts of Intelligent transportation systems (ITS) and their potential impact on traffic flow are discussed. State-of-the-art traffic flow research, microscopic traffic analysis, and traffic simulation have significantly advanced and are also discussed in this text. Real-world examples and useful problem sets complement each chapter.
A Compact Capstone Course in Classical Calculus
This textbook offers undergraduates a self-contained introduction to advanced topics not covered in a standard calculus sequence. The author's enthusiastic and engaging style makes this material, which typically requires a substantial amount of study, accessible to students with minimal prerequisites. Readers will gain a broad knowledge of the area, with approaches based on those found in recent literature, as well as historical remarks that deepen the exposition. Specific topics covered include the binomial theorem, the harmonic series, Euler's constant, geometric probability, and much more. Over the fifteen chapters, readers will discover the elegance of calculus and the pivotal role it plays within mathematics. A Compact Capstone Course in Classical Calculus is ideal for exploring interesting topics in mathematics beyond the standard calculus sequence, particularly for undergraduates who may not be taking more advanced math courses. It would also serve as a useful supplement for a calculus course and a valuable resource for self-study. Readers are expected to have completed two one-semester college calculus courses.
Tidy Finance with Python
This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with Python, we show how to conduct research in empirical finance from scratch.
Partial Least Squares Regression
Through envelopes, more has been learned about PLS regression, resulting in a mass of information that allows an envelope bridge that takes PLS regression from a black-box algorithm to a core statistical paradigm based on objective function optimization. This book develops this bridge.
Nonlinear Dynamics and Chaos
The goal of this Third Edition is the same as previous editions: to provide a good foundation - and a joyful experience - for anyone who'd like to learn about nonlinear dynamics and chaos from an applied perspective.
Confidence Intervals for Discrete Data in Clinical Research
There is only one published book on confidence interval for clinical research. This book has a cookbook style with several examples and codes so that methods presented in the book can be implemented. The primary audience will be statisticians.
Advanced Electromagnetic Wave Propagation Methods
This textbook provides a solid foundation into the approaches used in the analysis of complex electromagnetic problems and wave propagation. The techniques discussed are essential to obtain closed-form solutions or asymptotic solutions and meet an existing need for instructors and students in electromagnetic theory.
The Big Book of Real Analysis
This book provides an introduction to real analysis, a fundamental topic that is an essential requirement in the study of mathematics. It deals with the concepts of infinity and limits, which are the cornerstones in the development of calculus. Beginning with some basic proof techniques and the notions of sets and functions, the book rigorously constructs the real numbers and their related structures from the natural numbers. During this construction, the readers will encounter the notions of infinity, limits, real sequences, and real series. These concepts are then formalised and focused on as stand-alone objects. Finally, they are expanded to limits, sequences, and series of more general objects such as real-valued functions. Once the fundamental tools of the trade have been established, the readers are led into the classical study of calculus (continuity, differentiation, and Riemann integration) from first principles. The book concludes with an introduction to the studyof measures and how one can construct the Lebesgue integral as an extension of the Riemann integral. This textbook is aimed at undergraduate students in mathematics. As its title suggests, it covers a large amount of material, which can be taught in around three semesters. Many remarks and examples help to motivate and provide intuition for the abstract theoretical concepts discussed. In addition, more than 600 exercises are included in the book, some of which will lead the readers to more advanced topics and could be suitable for independent study projects. Since the book is fully self-contained, it is also ideal for self-study.
Introduction to Quantum Groups
This book introduces the reader to quantum groups, focusing on the simplest ones, namely the closed subgroups of the free unitary group.Although such quantum groups are quite easy to understand mathematically, interesting examples abound, including all classical Lie groups, their free versions, half-liberations, other intermediate liberations, anticommutation twists, the duals of finitely generated discrete groups, quantum permutation groups, quantum reflection groups, quantum symmetry groups of finite graphs, and more.The book is written in textbook style, with its contents roughly covering a one-year graduate course. Besides exercises, the author has included many remarks, comments and pieces of advice with the lone reader in mind. The prerequisites are basic algebra, analysis and probability, and a certain familiarity with complex analysis and measure theory. Organized in four parts, the book begins with the foundations of the theory, due to Woronowicz, comprising axioms, Haar measure, Peter-Weyl theory, Tannakian duality and basic Brauer theorems. The core of the book, its second and third parts, focus on the main examples, first in the continuous case, and then in the discrete case. The fourth and last part is an introduction to selected research topics, such as toral subgroups, homogeneous spaces and matrix models.Introduction to Quantum Groups offers a compelling introduction to quantum groups, from the simplest examples to research level topics.
Modern Discrete Probability
Providing a graduate-level introduction to discrete probability and its applications, this book develops a toolkit of essential techniques for analysing stochastic processes on graphs, other random discrete structures, and algorithms. Topics covered include the first and second moment methods, concentration inequalities, coupling and stochastic domination, martingales and potential theory, spectral methods, and branching processes. Each chapter expands on a fundamental technique, outlining common uses and showing them in action on simple examples and more substantial classical results. The focus is predominantly on non-asymptotic methods and results. All chapters provide a detailed background review section, plus exercises and signposts to the wider literature. Readers are assumed to have undergraduate-level linear algebra and basic real analysis, while prior exposure to graduate-level probability is recommended. This much-needed broad overview of discrete probability could serve as a textbook or as a reference for researchers in mathematics, statistics, data science, computer science and engineering.
