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 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.
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EAN: 9780367894368
UPC: 9780367894368
ISBN: 9780367894368
MPN: N/A
Item Length: 25.4 cm
Item Weight: 0.65 kg
Publisher: Taylor & Francis Ltd
Item Height: 254 mm
Subject: Mathematics
Publication Year: 2021
Number of Pages: 422 Pages
Publication Name: Bayesian Modeling and Computation in Python
Language: English
Type: Textbook
Author: Osvaldo A. Martin, Junpeng Lao, Ravin Kumar
Item Width: 178 mm
Format: Hardcover
