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Probability and Statistics for Computer Scientists, Third Edition
by Michael BaronPraise for the Second Edition: "The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter... [He] has taken great care to select examples that are interesting and practical for computer scientists. ... The content is illustrated with numerous figures, and concludes with appendices and an index. The book is erudite and … could work well as a required text for an advanced undergraduate or graduate course." ---Computing Reviews Probability and Statistics for Computer Scientists, Third Edition helps students understand fundamental concepts of Probability and Statistics, general methods of stochastic modeling, simulation, queuing, and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB, this classroom-tested book can be used for one- or two-semester courses. Features: Axiomatic introduction of probability Expanded coverage of statistical inference and data analysis, including estimation and testing, Bayesian approach, multivariate regression, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap Numerous motivating examples and exercises including computer projects Fully annotated R codes in parallel to MATLAB Applications in computer science, software engineering, telecommunications, and related areas In-Depth yet Accessible Treatment of Computer Science-Related TopicsStarting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). About the Author Michael Baron is David Carroll Professor of Mathematics and Statistics at American University in Washington D. C. He conducts research in sequential analysis and optimal stopping, change-point detection, Bayesian inference, and applications of statistics in epidemiology, clinical trials, semiconductor manufacturing, and other fields. M. Baron is a Fellow of the American Statistical Association and a recipient of the Abraham Wald Prize for the best paper in Sequential Analysis and the Regents Outstanding Teaching Award. M. Baron holds a Ph.D. in statistics from the University of Maryland. In his turn, he supervised twelve doctoral students, mostly employed on academic and research positions.
Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series)
by Norman MatloffProbability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: <P><P> * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. <P><P> Prerequisites are calculus, some matrix algebra, and some experience in programming. <P><P> Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
Probability and Statistics for Engineering and the Sciences
by Jay L. DevorePut statistical theories into practice with PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9th Edition. Always a favorite with statistics students, this calculus-based text offers a comprehensive introduction to probability and statistics while demonstrating how professionals apply concepts, models, and methodologies in today's engineering and scientific careers. <p><p>Jay Devore, an award-winning professor and internationally recognized author and statistician, emphasizes authentic problem scenarios in a multitude of examples and exercises, many of which involve real data, to show how statistics makes sense of the world. Mathematical development and derivations are kept to a minimum. <p><p>The book also includes output, graphics, and screen shots from various statistical software packages to give you a solid perspective of statistics in action. A Student Solutions Manual, which includes worked-out solutions to almost all the odd-numbered exercises in the book, is available.
Probability and Statistics for Engineering and the Sciences with Modeling using R (Textbooks in Mathematics)
by William P. Fox Rodney X. SturdivantProbability and statistics courses are more popular than ever. Regardless of your major or your profession, you will most likely use concepts from probability and statistics often in your career. The primary goal behind this book is offering the flexibility for instructors to build most undergraduate courses upon it. This book is designed for either a one-semester course in either introductory probability and statistics (not calculus-based) and/or a one-semester course in a calculus-based probability and statistics course. The book focuses on engineering examples and applications, while also including social sciences and more examples. Depending on the chapter flows, a course can be tailored for students at all levels and background. Over many years of teaching this course, the authors created problems based on real data, student projects, and labs. Students have suggested these enhance their experience and learning. The authors hope to share projects and labs with other instructors and students to make the course more interesting for both. R is an excellent platform to use. This book uses R with real data sets. The labs can be used for group work, in class, or for self-directed study. These project labs have been class-tested for many years with good results and encourage students to apply the key concepts and use of technology to analyze and present results.
Probability and Statistics for Engineers and Scientists (Ninth Edition)
by Ronald E. Walpole Sharon L. Myers Raymond H. Myers Keying E. YeThis classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance of theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.
Probability and Statistics for Machine Learning: A Textbook
by Charu C. AggarwalThis book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories: 1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning. The fundamentals of probability and statistics are covered in Chapters 2 through 5. 2. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapters 6 through 9 explore how different models from probability and statistics are applied to machine learning. Perhaps the most important tool that bridges the gap from data to probability is maximum-likelihood estimation, which is a foundational concept from the perspective of machine learning. This concept is explored repeatedly in these chapters. 3. Advanced topics: Chapter 10 is devoted to discrete-state Markov processes. It explores the application of probability and statistics to a temporal and sequential setting, although the applications extend to more complex settings such as graphical data. Chapter 11 covers a number of probabilistic inequalities and approximations. The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners.
Probability and Statistics for Particle Physics
by Carlos MañaThis book comprehensively presents the basic concepts of probability and Bayesian inference with sufficient generality to make them applicable to current problems in scientific research. The first chapter provides the fundamentals of probability theory that are essential for the analysis of random phenomena. The second chapter includes a full and pragmatic review of the Bayesian methods that constitute a natural and coherent framework with enough freedom to analyze all the information available from experimental data in a conceptually simple manner. The third chapter presents the basic Monte Carlo techniques used in scientific research, allowing a large variety of problems to be handled difficult to tackle by other procedures. The author also introduces a basic algorithm, which enables readers to simulate samples from simple distribution, and describes useful cases for researchers in particle physics.The final chapter is devoted to the basic ideas of Information Theory, which are important in the Bayesian methodology. This highly readable book is appropriate for graduate-level courses, while at the same time being useful for scientific researches in general and for physicists in particular since most of the examples are from the field of Particle Physics.
Probability and Statistics for STEM: A Course in One Semester (Synthesis Lectures on Mathematics & Statistics)
by Emmanuel N. Barron John G. Del GrecoThis new edition presents the essential topics in probability and statistics from a rigorous standpoint. Any discipline involving randomness, including medicine, engineering, and any area of scientific research, must have a way of analyzing or even predicting the outcomes of an experiment. The authors focus on the tools for doing so in a thorough, yet introductory way. After providing an overview of the basics of probability, the authors cover essential topics such as confidence intervals, hypothesis testing, and linear regression. These subjects are presented in a one semester format, suitable for engineers, scientists, and STEM students with a solid understanding of calculus. There are problems and exercises included in each chapter allowing readers to practice the applications of the concepts.
Probability and Statistics in the Physical Sciences (Undergraduate Texts in Physics)
by Byron P. RoeThis book, now in its third edition, offers a practical guide to the use of probability and statistics in experimental physics that is of value for both advanced undergraduates and graduate students. Focusing on applications and theorems and techniques actually used in experimental research, it includes worked problems with solutions, as well as homework exercises to aid understanding. Suitable for readers with no prior knowledge of statistical techniques, the book comprehensively discusses the topic and features a number of interesting and amusing applications that are often neglected. Providing an introduction to neural net techniques that encompasses deep learning, adversarial neural networks, and boosted decision trees, this new edition includes updated chapters with, for example, additions relating to generating and characteristic functions, Bayes’ theorem, the Feldman-Cousins method, Lagrange multipliers for constraints, estimation of likelihood ratios, and unfolding problems.
Probability and Stochastic Modeling
by Vladimir I. RotarA First Course in Probability with an Emphasis on Stochastic ModelingProbability and Stochastic Modeling not only covers all the topics found in a traditional introductory probability course, but also emphasizes stochastic modeling, including Markov chains, birth-death processes, and reliability models. Unlike most undergraduate-level probability t
Probability and Stochastic Processes
by Ionut FlorescuA comprehensive and accessible presentation of probability and stochastic processes with emphasis on key theoretical concepts and real-world applicationsWith a sophisticated approach, Probability and Stochastic Processes successfully balances theory and applications in a pedagogical and accessible format. The book's primary focus is on key theoretical notions in probability to provide a foundation for understanding concepts and examples related to stochastic processes.Organized into two main sections, the book begins by developing probability theory with topical coverage on probability measure; random variables; integration theory; product spaces, conditional distribution, and conditional expectations; and limit theorems. The second part explores stochastic processes and related concepts including the Poisson process, renewal processes, Markov chains, semi-Markov processes, martingales, and Brownian motion. Featuring a logical combination of traditional and complex theories as well as practices, Probability and Stochastic Processes also includes:Multiple examples from disciplines such as business, mathematical finance, and engineeringChapter-by-chapter exercises and examples to allow readers to test their comprehension of the presented materialA rigorous treatment of all probability and stochastic processes conceptsAn appropriate textbook for probability and stochastic processes courses at the upper-undergraduate and graduate level in mathematics, business, and electrical engineering, Probability and Stochastic Processes is also an ideal reference for researchers and practitioners in the fields of mathematics, engineering, and finance.
Probability and Stochastic Processes: A Volume in Honour of Rajeeva L. Karandikar (Indian Statistical Institute Series)
by B. V. Rao Siva Athreya Abhay G. BhattThe book collects papers on several topics in probability and stochastic processes. These papers have been presented at a conference organised in honour of Professor Rajeeva L. Karandikar who turned 65 in 2021. He was a distinguished researcher and a teacher at the Indian Statistical Institute (ISI), Delhi Centre, for many years. He has been a multi-faceted academician, interacting with the Government of India and the industry. He has left an indelible mark in every endeavour of his and in his several different avatars—be it in the ISI, in the industry or as Director of Chennai Mathematical Institute. This book will be useful to senior undergraduate and graduate students, as well as researchers in probability, statistics and related fields.
Probability And Stochastic Processes: A Friendly Introduction For Electrical And Computer Engineers
by Roy D. Yates David J. GoodmanIn Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, readers are able to grasp the concepts of probability and stochastic processes, and apply these in professional engineering practice. The 3rd edition also includes quiz solutions within the appendix of the text. The resource presents concepts clearly as a sequence of building blocks identified as an axiom, definition or theorem. This approach allows for a better understanding of the material, which can be utilized in solving practical problems.
Probability and Stochastic Processes for Physicists (UNITEXT for Physics)
by Nicola Cufaro PetroniThis book seeks to bridge the gap between the parlance, the models, and even the notations used by physicists and those used by mathematicians when it comes to the topic of probability and stochastic processes. The opening four chapters elucidate the basic concepts of probability, including probability spaces and measures, random variables, and limit theorems. Here, the focus is mainly on models and ideas rather than the mathematical tools. The discussion of limit theorems serves as a gateway to extensive coverage of the theory of stochastic processes, including, for example, stationarity and ergodicity, Poisson and Wiener processes and their trajectories, other Markov processes, jump-diffusion processes, stochastic calculus, and stochastic differential equations. All these conceptual tools then converge in a dynamical theory of Brownian motion that compares the Einstein–Smoluchowski and Ornstein–Uhlenbeck approaches, highlighting the most important ideas that finally led to a connection between the Schrödinger equation and diffusion processes along the lines of Nelson’s stochastic mechanics. A series of appendices cover particular details and calculations, and offer concise treatments of particular thought-provoking topics.
Probability Approximations and Beyond
by Andrew Barbour David Siegmund Hock Peng ChanIn June 2010, a conference, Probability Approximations and Beyond, was held at the National University of Singapore (NUS), in honor of pioneering mathematician Louis Chen. Chen made the first of several seminal contributions to the theory and application of Stein's method. One of his most important contributions has been to turn Stein's concentration inequality idea into an effective tool for providing error bounds for the normal approximation in many settings, and in particular for sums of random variables exhibiting only local dependence. This conference attracted a large audience that came to pay homage to Chen and to hear presentations by colleagues who have worked with him in special ways over the past 40+ years. The papers in this volume attest to how Louis Chen's cutting-edge ideas influenced and continue to influence such areas as molecular biology and computer science. He has developed applications of his work on Poisson approximation to problems of signal detection in computational biology. The original papers contained in this book provide historical context for Chen's work alongside commentary on some of his major contributions by noteworthy statisticians and mathematicians working today.
Probability Based High Temperature Engineering
by Leo RazdolskyThis volume on structural fire resistance is for aerospace, structural, and fire prevention engineers; architects, and educators. It bridges the gap between prescriptive- and performance-based methods and simplifies very complex and comprehensive computer analyses to the point that the structural fire resistance and high temperature creep deformations will have a simple, approximate analytical expression that can be used in structural analysis and design. The book emphasizes methods of the theory of engineering creep (stress-strain diagrams) and mathematical operations quite distinct from those of solid mechanics absent high-temperature creep deformations, in particular the classical theory of elasticity and structural engineering. Dr. Razdolsky s previous books focused on methods of computing the ultimate structural design load to the different fire scenarios. The current work is devoted to the computing of the estimated ultimate resistance of the structure taking into account the effect of high temperature creep deformations. An essential resource for aerospace structural engineers who wish to improve their understanding of structure exposed to flare up temperatures and severe fires, the book also serves as a textbook for introductory courses in fire safety in civil or structural engineering programs, vital reading for the PhD students in aerospace fire protection and structural engineering, and a case study of a number of high-profile fires (the World Trade Center, Broadgate Phase 8, One Meridian Plaza; Mandarin Towers). "Probability Based High Temperature Engineering: Creep and Structural Fire Resistance" successfully bridges the information gap between aerospace, structural, and engineers; building inspectors, architects, and code officials. "
Probability-Based Multi-objective Optimization for Material Selection
by Ying Cui Yi Wang Jie Yu Maosheng Zheng Haipeng TengThis book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection systematically, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies, such as response surface methodology, orthogonal experimental design, and uniform experimental design, are all performed; the conditions of the material performance utility with desirable value and robust assessment are included; the discretization treatment of complicated integral in the evaluation is presented. The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. This book can be used as a textbook for postgraduate and advanced undergraduate students in material relevant majors, and a reference book for scientists and engineers digging in the related fields.
Probability-Based Multi-objective Optimization for Material Selection
by Maosheng Zheng Jie Yu Haipeng Teng Ying Cui Yi WangThe second edition of this book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection in viewpoint of system theory, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies (response surface methodology, orthogonal experimental design, and uniform experimental design) are all performed; robustness assessment and performance utility with desirable value are included; discretization treatment in the evaluation is presented; fuzzy-based approach and cluster analysis are involved; applications in portfolio investment and shortest path problem are concerned as well. The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. It is designed to be used as a textbook for postgraduate and advanced undergraduate students in relevant majors, while also serving as a valuable reference book for scientists and engineers involved in related fields.
The Probability Companion for Engineering and Computer Science
by Adam Prügel-BennettThis friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overview for students.
Probability, Decisions and Games: A Gentle Introduction using R
by Abel Rodríguez Bruno MendesINTRODUCES THE FUNDAMENTALS OF PROBABILITY, STATISTICS, DECISION THEORY, AND GAME THEORY, AND FEATURES INTERESTING EXAMPLES OF GAMES OF CHANCE AND STRATEGY TO MOTIVATE AND ILLUSTRATE ABSTRACT MATHEMATICAL CONCEPTS Covering both random and strategic games, Probability, Decisions and Games features a variety of gaming and gambling examples to build a better understanding of basic concepts of probability, statistics, decision theory, and game theory. The authors present fundamental concepts such as random variables, rational choice theory, mathematical expectation and variance, fair games, combinatorial calculus, conditional probability, Bayes Theorem, Bernoulli trials, zero-sum games and Nash equilibria, as well as their application in games such as Roulette, Craps, Lotto, Blackjack, Poker, Rock-Paper-Scissors, the Game of Chicken and Tic-Tac-Toe. Computer simulations, implemented using the popular R computing environment, are used to provide intuition on key concepts and verify complex calculations. The book starts by introducing simple concepts that are carefully motivated by the same historical examples that drove their original development of the field of probability, and then applies those concepts to popular contemporary games. The first two chapters of Probability, Decisions and Games: A Gentle Introduction using R feature an introductory discussion of probability and rational choice theory in finite and discrete spaces that builds upon the simple games discussed in the famous correspondence between Blaise Pascal and Pierre de Fermat. Subsequent chapters utilize popular casino games such as Roulette and Blackjack to expand on these concepts illustrate modern applications of these methodologies. Finally, the book concludes with discussions on game theory using a number of strategic games. This book: · Features introductory coverage of probability, statistics, decision theory and game theory, and has been class-tested at University of California, Santa Cruz for the past six years · Illustrates basic concepts in probability through interesting and fun examples using a number of popular casino games: roulette, lotto, craps, blackjack, and poker · Introduces key ideas in game theory using classic games such as Rock-Paper-Scissors, Chess, and Tic-Tac-Toe. · Features computer simulations using R throughout in order to illustrate complex concepts and help readers verify complex calculations · Contains exercises and approaches games and gambling at a level that is accessible for readers with minimal experience · Adopts a unique approach by motivating complex concepts using first simple games and then moving on to more complex, well-known games that illustrate how these concepts work together Probability, Decisions and Games: A Gentle Introduction using R is a unique and helpful textbook for undergraduate courses on statistical reasoning, introduction to probability, statistical literacy, and quantitative reasoning for students from a variety of disciplines. ABEL RODRÍGUEZ, PhD, is Professor in the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz (UCSC), CA, USA. The author of 40 journal articles, his research interests include Bayesian nonparametric methods, machine learning, spatial temporal models, network models, and extreme value theory. BRUNO MENDES, PhD, is Lecturer in the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz, CA, USA. BRUNO MENDES, PhD, is Lecturer in the Department of Applied Mathematics and Statistics at the University of Cal
Probability Distributions: With Truncated, Log And Bivariate Extensions (SpringerBriefs In Statistics)
by Nick T. ThomopoulosThis volume presents a concise and practical overview of statistical methods and tables not readily available in other publications. It begins with a review of the commonly used continuous and discrete probability distributions. Several useful distributions that are not so common and less understood are described with examples and applications in full detail: discrete normal, left-partial, right-partial, left-truncated normal, right-truncated normal, lognormal, bivariate normal, and bivariate lognormal. Table values are provided with examples that enable researchers to easily apply the distributions to real applications and sample data. The left- and right-truncated normal distributions offer a wide variety of shapes in contrast to the symmetrically shaped normal distribution, and a newly developed spread ratio enables analysts to determine which of the three distributions best fits a particular set of sample data. The book will be highly useful to anyone who does statistical and probability analysis. This includes scientists, economists, management scientists, market researchers, engineers, mathematicians, and students in many disciplines.
Probability Distributions in Risk Management Operations
by Constantinos Artikis Panagiotis ArtikisThis book is about the formulations, theoretical investigations, and practical applications of new stochastic models for fundamental concepts and operations of the discipline of risk management. It also examines how these models can be useful in the descriptions, measurements, evaluations, and treatments of risks threatening various modern organizations. Moreover, the book makes clear that such stochastic models constitute very strong analytical tools which substantially facilitate strategic thinking and strategic decision making in many significant areas of risk management. In particular the incorporation of fundamental probabilistic concepts such as the sum, minimum, and maximum of a random number of continuous, positive, independent, and identically distributed random variables in the mathematical structure of stochastic models significantly supports the suitability of these models in the developments, investigations, selections, and implementations of proactive and reactive risk management operations. The book makes extensive use of integral and differential equations of characteristic functions, mainly corresponding to important classes of mixtures of probability distributions, as powerful analytical tools for investigating the behavior of new stochastic models suitable for the descriptions and implementations of fundamental risk control and risk financing operations. These risk treatment operations very often arise in a wide variety of scientific disciplines of extreme practical importance.
Probability For Analysts
by Karl StrombergThis book will enable researchers and students of analysis to more easily understand research papers in which probabilistic methods are used to prove theorems of analysis, many of which have no other known proofs. The book assumes a course in measure and integration theory but requires little or no background in probability theory. It emplhasizes topics of interest to analysts, including random series, martingales and Brownian motion.
Probability For Dummies
by Deborah J. RumseyWhether you're hitting the books for a probability or statistics course or hitting the tables at a casino, working out probabilities can be problematic. This book helps you even the odds. Using easy-to-understand explanations and examples, it demystifies probability -- and even offers savvy tips to boost your chances of gambling success!
Probability For Dummies
by Deborah J. RumseyLearn how to calculate your chances with easy-to-understand explanations of probability Probability—the likelihood or chance of an event occurring—is an important branch of mathematics used in business and economics, finance, engineering, physics, and beyond. We see probability at work every day in areas such as weather forecasting, investing, and sports betting. Packed with real-life examples and mathematical problems with thorough explanations, Probability For Dummies helps students, professionals, and the everyday reader learn the basics. Topics include set theory, counting, permutations and combinations, random variables, conditional probability, joint distributions, conditional expectations, and probability modeling. Pass your probability class and play your cards right, with this accessible Dummies guide. Understand how probability impacts daily life Discover what counting rules are and how to use them Practice probability concepts with sample problems and explanations Get clear explanations of all the topics in your probability or statistics class Probability For Dummies is the perfect Dummies guide for college students, amateur and professional gamblers, investors, insurance professionals, and anyone preparing for the actuarial exam.