Bayesian Modeling and Computation in Python - Chapman & Hall/CRC Texts in Statistical Science Series

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.

ISBN:	9780367894368
Publisher:	Taylor & Francis
Imprint:	Chapman & Hall/CRC
Published date:	29 Dec 2021
DEWEY:	519.542
DEWEY edition:	23
Language:	English
Number of pages:	422
Weight:	976g
Height:	182mm
Width:	366mm
Spine width:	27mm

Write a review

Your Name:

Your Review: Note: HTML is not translated!

Rating: Bad           Good

Enter the code in the box below: