Bayesian Statistics and Marketing (Wiley Series in Probability and Statistics)
by Peter E. Rossi, Greg M. Allenby, and Rob McCulloch
The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of th...
Bayesian Analysis for the Social Sciences (Wiley Series in Probability and Statistics, #846)
by Simon Jackman
Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any backgrou...
Linear Algebra and Probability for Computer Science Applications
by Ernest Davis
Based on the author's course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science. The course and the text are addressed to students with a very weak mathematical background. Most of the chapters discuss relevant MATLAB (R) functions and features and give sample assignments in MATLAB; the author's website provides the MATLAB code from the book. After an introductory chapte...
Bayesian Modeling and Computation in Python (Chapman & Hall/CRC Texts in Statistical Science)
by Osvaldo A. Martin, Ravin Kumar, and Junpeng Lao
Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of t...
Algorithms, Routines, and S-Functions for Robust Statistics
by Alfio Marazzi
ROBETH (written in ANSI FORTRAN 77) is a systematized collection of algorithms that allows computation of a broad class of procedures based on M- and high-breakdown point estimation, including robust regression, robust testing of linear hypotheses, and robust coveriances. This book describes the computational procedures included in ROBETH. Each chapter is organized into three parts: 1. An overview of the theoretical background for the statistical and numerical methods 2. A detailed descripti...
Sojourns and Extremes of Stochastic Processes is a research monograph in the area of probability theory. During the past thirty years Berman has made many contributions to the theory of the extreme values and sojourn times of the sample functions of broad classes of stochastic processes. These processes arise in theoretical and applied models, and are presented here in a unified exposition.
Modeling Survival Data Using Frailty Models (Industrial and Applied Mathematics)
by David D. Hanagal
When designing and analyzing a medical study, researchers focusing on survival data must take into account the heterogeneity of the study population: due to uncontrollable variation, some members change states more rapidly than others. Survival data measures the time to a certain event or change of state. For example, the event may be death, occurr
Bayes Rules! (Chapman & Hall/CRC Texts in Statistical Science)
by Alicia A Johnson, Miles Q. Ott, and Mine Dogucu
Utilizes data driven examples and exercises. Emphasizes the iterative model building and evaluation process. Surveys an interconnected range of multivariable regression and classification models. Presents fundamental Markov chain Monte Carlo simulation techniques for Bayesian models.
Dynamical Search (Chapman & Hall/CRC Interdisciplinary Statistics, #7)
by Luc Pronzato, Henry P. Wynn, and Anatoly A. Zhigljavsky
Certain algorithms that are known to converge can be renormalized or "blown up" at each iteration so that their local behavior can be seen. This creates dynamical systems that we can study with modern tools, such as ergodic theory, chaos, special attractors, and Lyapounov exponents. Furthermore, we can translate the rates of convergence into less studied exponents known as Renyi entropies. This all feeds back to suggest new algorithms with faster rates of convergence. For example, in line-searc...
Stochastic Processes and Filtering Theory (Dover Books on Electrical Engineering) (Mathematics in Science and Engineering)
by Andrew H Jazwinski
Essentials of Inferential Statistics
by Malcolm O Asadoorian and Demetri Kantarelis
This fifth edition of a classic text is appropriate for a one semester general course in Applied Statistics or as a reference book for practicing researchers in a wide variety of disciplines, including medicine, health and human services, natural and social sciences, law, and engineering. This practical book describes the Bayesian principles necessary for applied clinical research and strategic interaction, which are frequently omitted in other texts. After a comprehensive treatment of probabi...
Polya Urn Models (Chapman & Hall/CRC Texts in Statistical Science)
by Hosam Mahmoud
Incorporating a collection of recent results, Polya Urn Models deals with discrete probability through the modern and evolving urn theory and its numerous applications. The book first substantiates the realization of distributions with urn arguments and introduces several modern tools, including exchangeability and stochastic processes via urns. It reviews classical probability problems and presents dichromatic Polya urns as a basic discrete structure growing in discrete time. The author then e...
Applied Bayesian Modelling (Wiley Series in Probability and Statistics, #394)
by Peter Congdon
The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author's best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS - a popular software package that off...
If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not man...
Probability and Statistics with R
by Maria Dolores Ugarte, Ana F. Militino, and Alan T. Arnholt
Cohesively Incorporates Statistical Theory with R ImplementationSince the publication of the popular first edition of this comprehensive textbook, the contributed R packages on CRAN have increased from around 1,000 to over 6,000. Designed for an intermediate undergraduate course, Probability and Statistics with R, Second Edition explores how some o
A unique volume covering the author's research over the last decade in this exciting area.
Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of spatial and spatio-temporal data in order to make wide ranging inference and prediction. Ideally such inferential tasks should be approached through modelling, which aids in estimation of uncertainties in all conclusions drawn from such data. Unified Bayesian modelling, implemented through user friendly so...
Event Attendance Prediction in Social Networks (SpringerBriefs in Statistics)
by Xiaomei Zhang and Guohong Cao
This volume focuses on predicting users' attendance at a future event at specific time and location based on their common interests. Event attendance prediction has attracted considerable attention because of its wide range of potential applications. By predicting event attendance, events that better fit users' interests can be recommended, and personalized location-based or topic-based services related to the events can be provided to users. Moreover, it can help event organizers estimating the...
Multistage Selection and Ranking Procedures (Statistics: A Series of Textbooks and Monographs, #142)
"This useful volume provides a thorough synthesis of second-order asymptotics in multistage sampling methodologies for selection and ranking unifying available second-order results in general and applying them to a host of situations Contains, in each chapter, helpful Notes and Overviews to facilitate comprehension, as well as Complements and Problems for more in-depth study of specific topics!"
Making decisions is a ubiquitous mental activity in our private and professional or public lives. It entails choosing one course of action from an available shortlist of options. Statistics for Making Decisions places decision making at the centre of statistical inference, proposing its theory as a new paradigm for statistical practice. The analysis in this paradigm is earnest about prior information and the consequences of the various kinds of errors that may be committed. Its conclusion is a c...
This book presents a comprehensive treatment of pricing and hedging soccer betting contracts using the modern techniques of probability, statistics, finance, and portfolio management. It introduces betting in general and then addresses the probabilistic intuition needed in the more complex world of soccer betting. The author presents an innovative perspective by thinking about betting odds in terms of the expected number of goals of both teams. In addition, when the betting market is in play, th...