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

Martin, Osvaldo A.,Kumar, Ravin,Lao, Junpeng

ISBN 10: 036789436X / ISBN 13: 9780367894368

Verlag: Chapman and Hall/CRC, 2021

Beschreibung

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Bibliografische Details

Titel: Bayesian Modeling and Computation in Python ...

Verlag: Chapman and Hall/CRC

Erscheinungsdatum: 2021

Einband: hardcover

Zustand: Very Good

Über diesen Titel

Inhaltsangabe:

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.

Über die Autorin bzw. den Autor:

Osvaldo A. Martin is a Researcher at IMASL-CONICET in Argentina and the Department of Computer Science from Aalto University in Finland. He has a PhD in biophysics and structural bioinformatics. Over the years he has become increasingly interested in data analysis problems with a Bayesian flavor. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling.

Ravin Kumar is a Data Scientist at Google and previously worked at SpaceX and sweetgreen among other companies. He has an M.S in Manufacturing Engineering and a B.S in Mechanical Engineering. He found Bayesian statistics to be an excellent tool for modeling organizations and informing strategy. This interest in flexible statistical modeling led to a warm welcoming open source community which he is honored to be a member of now.

Junpeng Lao is a Data Scientist at Google. Prior to that he did his PhD and subsequently worked as a postdoc in Cognitive Neuroscience. He developed a fondness for Bayesian Statistics and generative modeling after working primarily with Bootstrapping and Permutation during his academic life.