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010 a| 2021037791;

020 a| 9780367894368q| (hardback);

020 a| 9781032180298q| (paperback);

020 z| 9781003019169q| (ebook);

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100 1 a| Martin, Osvaldo.?| UNAUTHORIZED;

245 10 a| Bayesian modeling and computation in Pythonh| [electronic resource] /c| Osvaldo A. Martin, Ravin Kumar, Junpeng Lao.;

260 a| Boca Raton :b| CRC Press,c| 2022.;

263 a| 2201;

300 a| pages cm.;

490 0 a| Texts in statistical science;

504 a| Includes bibliographical references and index.;

506 a| Available only to authorized users.;

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.;

538 a| Mode of access: World Wide Web;

650 0 a| Bayesian statistical decision theory.=| ^A20878;

650 0 a| Python (Computer program language)=| ^A809520;

650 0 a| Mathematical statistics.=| ^A5024;

655 0 a| Electronic books.=| ^A491897;

856 40 z| Full text available from Taylor & Francis eBooksu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.taylorfrancis.com%2Fbooks%2F9781003019169;

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Bayesian modeling and computation in Python / Osvaldo A. Martin, Ravin Kumar, Junpeng Lao.

Author/creator	Martin, Osvaldo
Format	Electronic
Publication Info	Boca Raton : CRC Press, 2022.
Description	pages cm.
Supplemental Content	Full text available from Taylor & Francis eBooks
Subjects	Bayesian statistical decision theory. Python (Computer program language) Mathematical statistics.

Series	Texts in statistical science
Abstract	"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"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2021037791
ISBN	9780367894368 (hardback)
ISBN	9781032180298 (paperback)
ISBN	(ebook)

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Bayesian modeling and computation in Python / Osvaldo A. Martin, Ravin Kumar, Junpeng Lao.

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