Bayesian Statistics For Experimental Scientists
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Bayesian Statistics For Experimental Scientists
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Author : Richard A. Chechile
language : en
Publisher: MIT Press
Release Date : 2020-09-08
Bayesian Statistics For Experimental Scientists written by Richard A. Chechile and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-08 with Mathematics categories.
An introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis. This book offers an introduction to the Bayesian approach to statistical inference, with a focus on nonparametric and distribution-free methods. It covers not only well-developed methods for doing Bayesian statistics but also novel tools that enable Bayesian statistical analyses for cases that previously did not have a full Bayesian solution. The book's premise is that there are fundamental problems with orthodox frequentist statistical analyses that distort the scientific process. Side-by-side comparisons of Bayesian and frequentist methods illustrate the mismatch between the needs of experimental scientists in making inferences from data and the properties of the standard tools of classical statistics.
Bayesian Statistics For Experimental Scientists
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Author : Richard A. Chechile
language : en
Publisher: MIT Press
Release Date : 2020-09-08
Bayesian Statistics For Experimental Scientists written by Richard A. Chechile and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-08 with Mathematics categories.
An introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis. This book offers an introduction to the Bayesian approach to statistical inference, with a focus on nonparametric and distribution-free methods. It covers not only well-developed methods for doing Bayesian statistics but also novel tools that enable Bayesian statistical analyses for cases that previously did not have a full Bayesian solution. The book's premise is that there are fundamental problems with orthodox frequentist statistical analyses that distort the scientific process. Side-by-side comparisons of Bayesian and frequentist methods illustrate the mismatch between the needs of experimental scientists in making inferences from data and the properties of the standard tools of classical statistics. The book first covers elementary probability theory, the binomial model, the multinomial model, and methods for comparing different experimental conditions or groups. It then turns its focus to distribution-free statistics that are based on having ranked data, examining data from experimental studies and rank-based correlative methods. Each chapter includes exercises that help readers achieve a more complete understanding of the material. The book devotes considerable attention not only to the linkage of statistics to practices in experimental science but also to the theoretical foundations of statistics. Frequentist statistical practices often violate their own theoretical premises. The beauty of Bayesian statistics, readers will learn, is that it is an internally coherent system of scientific inference that can be proved from probability theory.
Bayesian Data Analysis For The Behavioral And Neural Sciences
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Author : Todd E. Hudson
language : en
Publisher: Cambridge University Press
Release Date : 2021-06-30
Bayesian Data Analysis For The Behavioral And Neural Sciences written by Todd E. Hudson and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-30 with Psychology categories.
This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond "frequentist" concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called "hypothesis testing") problems most frequently encountered in real-world applications.
Introduction To Bayesian Statistics
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Author : William M. Bolstad
language : en
Publisher: John Wiley & Sons
Release Date : 2016-09-02
Introduction To Bayesian Statistics written by William M. Bolstad and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-02 with Mathematics categories.
"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. Introduction to Bayesian Statistics, Third Edition also features: Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.
Bayesian Statistics And Marketing
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Author : Peter E. Rossi
language : en
Publisher: John Wiley & Sons
Release Date : 2012-05-14
Bayesian Statistics And Marketing written by Peter E. Rossi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-14 with Mathematics categories.
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 the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods. Written by the leading experts in the field, this unique book: Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models. Provides a self-contained introduction to Bayesian methods. Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems. Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies. Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.
Bayesian Data Analysis Third Edition
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Author : Andrew Gelman
language : en
Publisher: CRC Press
Release Date : 2013-11-01
Bayesian Data Analysis Third Edition written by Andrew Gelman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-01 with Mathematics categories.
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Bayesian Statistics For Beginners
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Author : Therese M. Donovan
language : en
Publisher:
Release Date : 2019
Bayesian Statistics For Beginners written by Therese M. Donovan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Mathematics categories.
Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources. Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields.
Analyzing Memory
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Author : Richard A. Chechile
language : en
Publisher: MIT Press
Release Date : 2024-09-17
Analyzing Memory written by Richard A. Chechile and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-17 with Psychology categories.
An accessible synthesis of memory research that discusses the creation of memory representations, the processes of storage and retrieval, and the effectiveness of encoding information. The field of memory research is subdivided into many separate and non-overlapping topic areas that often employ specialized tools and models. This book offers an accessible synthesis of memory research that explores how memory works, how it is organized, and how it changes dynamically. Written by an expert in the field, it can be used by undergraduate and graduate students of psychology and as a reference by researchers who want to fill in gaps in their knowledge. The book focuses on three general topics that cover a vast amount of research in the field: how a memory representation is created, how the cognitive processes of storage and retrieval can be studied and measured, and the process of encoding information and its varying degrees of effectiveness. Specific subjects addressed include habituation and sensitization, and the neurobiological changes that underlie them; evidence for a cognitive component underlying Pavlovian conditioning; biological constraints on a cognitive model of memory; an information-processing framework for memory; misconceptions about memory, including the static memory myth and the permanent memory myth; model-based measurement of storage and retrieval processes; a critique of the concept of memory strength; the distinction between implicit and explicit memory; and learning and repetition. Although the writing is accessible to the nonspecialist, the density of information is high. The text avoids jargon, and a glossary defines key terms. The notes expand on technical details and point to interesting related ideas.
Bayesian Methods
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Author : Jeff Gill
language : en
Publisher: CRC Press
Release Date : 2007-11-26
Bayesian Methods written by Jeff Gill and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-11-26 with Mathematics categories.
The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorporates the latest methodology and recent changes in software offerings. New to the Second Edition Two chapters on Markov chain Monte Carlo (MCMC) that cover ergodicity, convergence, mixing, simulated annealing, reversible jump MCMC, and coupling Expanded coverage of Bayesian linear and hierarchical models More technical and philosophical details on prior distributions A dedicated R package (BaM) with data and code for the examples as well as a set of functions for practical purposes such as calculating highest posterior density (HPD) intervals Requiring only a basic working knowledge of linear algebra and calculus, this text is one of the few to offer a graduate-level introduction to Bayesian statistics for social scientists. It first introduces Bayesian statistics and inference, before moving on to assess model quality and fit. Subsequent chapters examine hierarchical models within a Bayesian context and explore MCMC techniques and other numerical methods. Concentrating on practical computing issues, the author includes specific details for Bayesian model building and testing and uses the R and BUGS software for examples and exercises.
Designed Experiments For Science And Engineering
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Author : Michael D. Holloway
language : en
Publisher: CRC Press
Release Date : 2024-12-19
Designed Experiments For Science And Engineering written by Michael D. Holloway and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-19 with Science categories.
Designed Experiments for Science and Engineering is a versatile and overarching toolkit that explores various methods of designing experiments for over 20 disciplines in science and engineering. Designed experiments provide a structured approach to hypothesis testing, data analysis, and decision‐making. They allow researchers and engineers to efficiently explore multiple factors, interactions, and their impact on outcomes, ultimately leading to better‐designed processes, products, and systems across a wide range of scientific and engineering disciplines. Each discipline covered in this book includes the key characteristics of the steps in choosing and executing the experimental designs (one factor, fractional factorial, mixture experimentation, factor central composite, 3‐factor + central composite, etc.) and reviews the various statistical tools used as well as the steps in how to utilize each (standard deviation analysis, analysis of variance [ANOVA], relative standard deviation, bias analysis, etc.). This book is essential reading for students and professionals who are involved in research and development within various fields in science and engineering, such as mechanical engineering, environmental science, manufacturing, and aerospace engineering.