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Bayesian modeling and computation in Python

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian modeling and computation in PythonOsvaldo A. Martin, Ravin Kumar and Junpeng Lao.
作者:	Martin, Osvaldo.
其他作者:	Kumar, Ravin.
出版者:	Boca Raton, FL :CRC Press,2022.
面頁冊數:	1 online resource.
附註:	"A Chapman & Hall book."
標題:	Bayesian statistical decision theory.
電子資源:	https://www.taylorfrancis.com/books/9781003019169
ISBN:	9781003019169$q(ebook)

Bayesian modeling and computation in Python
Martin, Osvaldo.

Bayesian modeling and computation in Python[electronic resource] /Osvaldo A. Martin, Ravin Kumar and Junpeng Lao. - 1st ed. - Boca Raton, FL :CRC Press,2022. - 1 online resource. - Texts in statistical science. - Texts in statistical science..

"A Chapman & Hall book."

Includes bibliographical references and index.

"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: 9781003019169$q(ebook)Subjects--Topical Terms:

182005
Bayesian statistical decision theory.

LC Class. No.: QA279.5

Dewey Class. No.: 519.5/42

Bayesian modeling and computation in Python
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245 1 0 $a Bayesian modeling and computation in Python $h [electronic resource] / $c Osvaldo A. Martin, Ravin Kumar and Junpeng Lao.
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260 $a Boca Raton, FL : $b CRC Press, $c 2022.
300 $a 1 online resource.
490 1 $a Texts in statistical science
500 $a "A Chapman & Hall book."
504 $a Includes bibliographical references and index.
520 $a "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"-- $c Provided by publisher.
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650 0 $a Bayesian statistical decision theory. $3 182005
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