Download the fantastic book titled Bayesian Modeling and Computation in Python written by Osvaldo A. Martin, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Bayesian Modeling and Computation in Python", which was released on 28 December 2021. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.
Summary of Bayesian Modeling and Computation in Python by Osvaldo A. Martin PDF
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.
Detail About Bayesian Modeling and Computation in Python PDF
- Author : Osvaldo A. Martin
- Publisher : CRC Press
- Genre : Computers
- Total Pages : 420 pages
- ISBN : 1000520048
- Release Date : 28 December 2021
- PDF File Size : 24,7 Mb
- Language : English
- Rating : 4/5 from 21 reviews
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