Bayesian modeling and computation in Python
"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 …
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- Full title :Bayesian modeling and computation in Python / Osvaldo A. Martin, Ravin Kumar and Junpeng Lao.
Summary
"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 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 internals 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"--
Read More ↓Edition Statements
- Publication Date:©2022
Publication Statements
- Publisher(s):A Chapman & Hall Book, CRC Press
- Place of Publication:Boca Raton, FL
- Language:eng
- Publication Date:©2022
The Physical Object
- Description:1 online resource : : illustrations (some color).
Edition Identifiers
- ISBN:9781003019169, 1003019161, 9781000520040, 1000520048, 1000520072, 9781000520071
- oclc:on1269097692
- LC Classification:QA279.5
- Dewey Decimal:519.5/42
Table of Contents
ForewordPrefaceSymbolsChapter 1 Bayesian InferenceChapter 2 Exploratory Analysis of Bayesian ModelsChapter 3 Linear Models and Probabilistic Programming LanguagesChapter 4 Extending Linear ModelsChapter 5 SplinesChapter 6 Time SeriesChapter 7 Bayesian Additive Regression TreesChapter 8 Approximate Bayesian ComputationChapter 9End to End Bayesian WorkflowsChapter 10 Probabilistic Programming LanguagesChapter 11 Appendiceal TopicsGlossaryBibliographyIndex
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