Waic And Wbic With R Stan

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WAIC and WBIC with R Stan

Author : Joe Suzuki
Publisher : Springer Nature
ISBN 13 : 9819938384
Total Pages : 241 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis WAIC and WBIC with R Stan by : Joe Suzuki

Download or read book WAIC and WBIC with R Stan written by Joe Suzuki and published by Springer Nature. This book was released on 2023-11-25 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. This book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in R and Stan. Whether you’re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory. The key features of this indispensable book include: A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise. 100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension. A comprehensive guide to Sumio Watanabe’s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians. Detailed source programs and Stan codes that will enhance readers’ grasp of the mathematical concepts presented. A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting. Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!

WAIC and WBIC with Python Stan

Author : Joe Suzuki
Publisher : Springer Nature
ISBN 13 : 9819938414
Total Pages : 249 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis WAIC and WBIC with Python Stan by : Joe Suzuki

Download or read book WAIC and WBIC with Python Stan written by Joe Suzuki and published by Springer Nature. This book was released on 2024-01-09 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. The book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in Python and Stan. Whether you’re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory. The key features of this indispensable book include: A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise. 100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension. A comprehensive guide to Sumio Watanabe’s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians. Detailed source programs and Stan codes that will enhance readers’ grasp of the mathematical concepts presented. A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting. Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!

Bayesian Statistical Modeling with Stan, R, and Python

Author : Kentaro Matsuura
Publisher : Springer Nature
ISBN 13 : 9811947554
Total Pages : 395 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Bayesian Statistical Modeling with Stan, R, and Python by : Kentaro Matsuura

Download or read book Bayesian Statistical Modeling with Stan, R, and Python written by Kentaro Matsuura and published by Springer Nature. This book was released on 2023-01-24 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language. The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines. Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.

Advancements in Bayesian Methods and Implementations

Author :
Publisher : Academic Press
ISBN 13 : 0323952690
Total Pages : 322 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Advancements in Bayesian Methods and Implementations by :

Download or read book Advancements in Bayesian Methods and Implementations written by and published by Academic Press. This book was released on 2022-10-06 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancements in Bayesian Methods and Implementation, Volume 47 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Fisher Information, Cramer-Rao and Bayesian Paradigm, Compound beta binomial distribution functions, MCMC for GLMMS, Signal Processing and Bayesian, Mathematical theory of Bayesian statistics where all models are wrong, Machine Learning and Bayesian, Non-parametric Bayes, Bayesian testing, and Data Analysis with humans, Variational inference or Functional horseshoe, Generalized Bayes. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Handbook of Statistics series - Updated release includes the latest information on Advancements in Bayesian Methods and Implementation

Bayesian Hierarchical Models

Author : Peter D. Congdon
Publisher : CRC Press
ISBN 13 : 0429532903
Total Pages : 487 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Bayesian Hierarchical Models by : Peter D. Congdon

Download or read book Bayesian Hierarchical Models written by Peter D. Congdon and published by CRC Press. This book was released on 2019-09-16 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website

Bayesian Data Analysis, Third Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Mathematical Theory of Bayesian Statistics

Author : Sumio Watanabe
Publisher : CRC Press
ISBN 13 : 148223808X
Total Pages : 331 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Mathematical Theory of Bayesian Statistics by : Sumio Watanabe

Download or read book Mathematical Theory of Bayesian Statistics written by Sumio Watanabe and published by CRC Press. This book was released on 2018-04-27 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Theory of Bayesian Statistics introduces the mathematical foundation of Bayesian inference which is well-known to be more accurate in many real-world problems than the maximum likelihood method. Recent research has uncovered several mathematical laws in Bayesian statistics, by which both the generalization loss and the marginal likelihood are estimated even if the posterior distribution cannot be approximated by any normal distribution. Features Explains Bayesian inference not subjectively but objectively. Provides a mathematical framework for conventional Bayesian theorems. Introduces and proves new theorems. Cross validation and information criteria of Bayesian statistics are studied from the mathematical point of view. Illustrates applications to several statistical problems, for example, model selection, hyperparameter optimization, and hypothesis tests. This book provides basic introductions for students, researchers, and users of Bayesian statistics, as well as applied mathematicians. Author Sumio Watanabe is a professor of Department of Mathematical and Computing Science at Tokyo Institute of Technology. He studies the relationship between algebraic geometry and mathematical statistics.

Python Workout

Author : Reuven M. Lerner
Publisher : Manning
ISBN 13 : 1617295507
Total Pages : 246 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Python Workout by : Reuven M. Lerner

Download or read book Python Workout written by Reuven M. Lerner and published by Manning. This book was released on 2020-08-04 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: The only way to master a skill is to practice. In Python Workout, author Reuven M. Lerner guides you through 50 carefully selected exercises that invite you to flex your programming muscles. As you take on each new challenge, you’ll build programming skill and confidence. Summary The only way to master a skill is to practice. In Python Workout, author Reuven M. Lerner guides you through 50 carefully selected exercises that invite you to flex your programming muscles. As you take on each new challenge, you’ll build programming skill and confidence. The thorough explanations help you lock in what you’ve learned and apply it to your own projects. Along the way, Python Workout provides over four hours of video instruction walking you through the solutions to each exercise and dozens of additional exercises for you to try on your own. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology To become a champion Python programmer you need to work out, building mental muscle with your hands on the keyboard. Each carefully selected exercise in this unique book adds to your Python prowess—one important skill at a time. About the book Python Workout presents 50 exercises that focus on key Python 3 features. In it, expert Python coach Reuven Lerner guides you through a series of small projects, practicing the skills you need to tackle everyday tasks. You’ll appreciate the clear explanations of each technique, and you can watch Reuven solve each exercise in the accompanying videos. What's inside 50 hands-on exercises and solutions Coverage of all Python data types Dozens more bonus exercises for extra practice About the reader For readers with basic Python knowledge. About the author Reuven M. Lerner teaches Python and data science to companies around the world. Table of Contents 1 Numeric types 2 Strings 3 Lists and tuples 4 Dictionaries and sets 5 Files 6 Functions 7 Functional programming with comprehensions 8 Modules and packages 9 Objects 10 Iterators and generators

The Oxford Handbook of Computational and Mathematical Psychology

Author : Jerome R. Busemeyer
Publisher :
ISBN 13 : 0199957991
Total Pages : 425 pages
Book Rating : 4.1/5 (999 download)

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Book Synopsis The Oxford Handbook of Computational and Mathematical Psychology by : Jerome R. Busemeyer

Download or read book The Oxford Handbook of Computational and Mathematical Psychology written by Jerome R. Busemeyer and published by . This book was released on 2015 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Oxford Handbook offers a comprehensive and authoritative review of important developments in computational and mathematical psychology. With chapters written by leading scientists across a variety of subdisciplines, it examines the field's influence on related research areas such as cognitive psychology, developmental psychology, clinical psychology, and neuroscience. The Handbook emphasizes examples and applications of the latest research, and will appeal to readers possessing various levels of modeling experience. The Oxford Handbook of Computational and mathematical Psychology covers the key developments in elementary cognitive mechanisms (signal detection, information processing, reinforcement learning), basic cognitive skills (perceptual judgment, categorization, episodic memory), higher-level cognition (Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (Bayesian estimation and other new model comparison methods), and emerging new directions in computation and mathematical psychology (neurocognitive modeling, applications to clinical psychology, quantum cognition). The Handbook would make an ideal graduate-level textbook for courses in computational and mathematical psychology. Readers ranging from advanced undergraduates to experienced faculty members and researchers in virtually any area of psychology--including cognitive science and related social and behavioral sciences such as consumer behavior and communication--will find the text useful.

Model Selection and Model Averaging

Author : Gerda Claeskens
Publisher :
ISBN 13 : 9780521852258
Total Pages : 312 pages
Book Rating : 4.8/5 (522 download)

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Book Synopsis Model Selection and Model Averaging by : Gerda Claeskens

Download or read book Model Selection and Model Averaging written by Gerda Claeskens and published by . This book was released on 2008-07-28 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: First book to synthesize the research and practice from the active field of model selection.

Bayesian Population Analysis Using WinBUGS

Author : Marc Kéry
Publisher : Academic Press
ISBN 13 : 0123870208
Total Pages : 556 pages
Book Rating : 4.1/5 (238 download)

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Book Synopsis Bayesian Population Analysis Using WinBUGS by : Marc Kéry

Download or read book Bayesian Population Analysis Using WinBUGS written by Marc Kéry and published by Academic Press. This book was released on 2012 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist All WinBUGS/OpenBUGS analyses are completely integrated in software R Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R

Handbook of Markov Chain Monte Carlo

Author : Steve Brooks
Publisher : CRC Press
ISBN 13 : 1420079425
Total Pages : 620 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Handbook of Markov Chain Monte Carlo by : Steve Brooks

Download or read book Handbook of Markov Chain Monte Carlo written by Steve Brooks and published by CRC Press. This book was released on 2011-05-10 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie

The BUGS Book

Author : David Lunn
Publisher : CRC Press
ISBN 13 : 1466586664
Total Pages : 393 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis The BUGS Book by : David Lunn

Download or read book The BUGS Book written by David Lunn and published by CRC Press. This book was released on 2012-10-02 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents

Algebraic Geometry and Statistical Learning Theory

Author : Sumio Watanabe
Publisher : Cambridge University Press
ISBN 13 : 0521864674
Total Pages : 295 pages
Book Rating : 4.5/5 (218 download)

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Book Synopsis Algebraic Geometry and Statistical Learning Theory by : Sumio Watanabe

Download or read book Algebraic Geometry and Statistical Learning Theory written by Sumio Watanabe and published by Cambridge University Press. This book was released on 2009-08-13 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sure to be influential, Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

Author : Franzi Korner-Nievergelt
Publisher : Academic Press
ISBN 13 : 0128016787
Total Pages : 329 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan by : Franzi Korner-Nievergelt

Download or read book Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan written by Franzi Korner-Nievergelt and published by Academic Press. This book was released on 2015-04-04 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. - Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest - Written in a step-by-step approach that allows for eased understanding by non-statisticians - Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data - All example data as well as additional functions are provided in the R-package blmeco

Bayesian Models for Astrophysical Data

Author : Joseph M. Hilbe
Publisher : Cambridge University Press
ISBN 13 : 1108210740
Total Pages : 429 pages
Book Rating : 4.1/5 (82 download)

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Book Synopsis Bayesian Models for Astrophysical Data by : Joseph M. Hilbe

Download or read book Bayesian Models for Astrophysical Data written by Joseph M. Hilbe and published by Cambridge University Press. This book was released on 2017-04-27 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

Bayesian Cognitive Modeling

Author : Michael D. Lee
Publisher : Cambridge University Press
ISBN 13 : 1107653916
Total Pages : 279 pages
Book Rating : 4.1/5 (76 download)

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Book Synopsis Bayesian Cognitive Modeling by : Michael D. Lee

Download or read book Bayesian Cognitive Modeling written by Michael D. Lee and published by Cambridge University Press. This book was released on 2014-04-03 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.