Bayesian Modeling and Computation in Python (Chapman & Hall/CRC Texts in Statistical Science)

by: Osvaldo A. Martin (Author),Ravin Kumar(Author),Junpeng Lao(Author)&0more

Publisher: Chapman and Hall/CRC

Edition: 1st

Publication Date: 2021/12/29

Language: English

Print Length: 398 pages

ISBN-10: 036789436X

ISBN-13: 9780367894368

Book Description

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 mode methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the inteals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics.This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.

About the Author

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 mode methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the inteals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics.This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.

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