Bayesian modeling and computation in Python [electronic resource] / Osvaldo A. Martin, Ravin Kumar and Junpeng Lao.
- 作者： Martin, Osvaldo.
- 其他作者：
- 其他題名：
- Texts in statistical science.
- 出版： Boca Raton, FL : CRC Press 2022.
- 叢書名： Texts in statistical science
- 主題： Bayesian statistical decision theory. , Python (Computer program language) , Mathematical statistics.
- 版本：1st ed.
- ISBN： 9781003019169 (ebook) 、 1003019161 (ebook) 、 9781000520040 (ePDF) 、 1000520048 (ePDF) 、 9781000520071 (epub) 、 1000520072 (epub)
- URL：
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- 一般註："A Chapman & Hall book." Includes bibliographical references and index. 112年度臺灣學術電子書暨資料庫聯盟採購
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讀者標籤：
- 系統號： 000304465 | 機讀編目格式
館藏資訊
"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"--
摘要註
"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"--
