Bayesian Modeling and Computation in Python

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.

The e-book version has been corrected and reflowed. Please contact the seller if the updated version has not come through.

《Python中的貝葉斯建模與計算》旨在幫助初學者貝葉斯實踐者成為中級模型師。本書採用實踐為主的方法，使用PyMC3、Tensorflow Probability、ArviZ等庫，著重於應用統計學的實踐，並參考其底層的數學理論。

本書首先回顧了貝葉斯推斷的概念。第二章介紹了貝葉斯模型的現代探索性分析方法。在掌握了這兩個基礎後，後續章節將介紹各種模型，包括線性回歸、樣條函數、時間序列、貝葉斯加法回歸樹等。最後幾章包括近似貝葉斯計算、端到端案例研究，展示如何在不同場景中應用貝葉斯建模，以及一章關於概率編程語言的內部結構。最後一章則作為本書的參考資料，更深入地探討數學方面或擴展某些主題的討論。

本書由PyMC3、ArviZ、Bambi和Tensorflow Probability等庫的貢獻者共同撰寫。

電子書版本已經經過修正和重新排版。如果未收到更新版本，請聯繫賣方。

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.

Osvaldo A. Martin 是阿根廷 IMASL-CONICET 的研究員，也是芬蘭 Aalto 大學計算機科學系的研究員。他擁有生物物理學和結構生物信息學的博士學位。多年來，他對具有貝葉斯風格的數據分析問題越來越感興趣。他特別關注開發和實施用於貝葉斯統計和概率建模的軟件工具。

Ravin Kumar 是 Google 的數據科學家，之前曾在 SpaceX 和 sweetgreen 等公司工作。他擁有製造工程的碩士學位和機械工程的學士學位。他發現貝葉斯統計是一個優秀的工具，可以對組織進行建模並提供戰略信息。這種對靈活統計建模的興趣使他加入了一個熱情友好的開源社區，他很榮幸成為其中的一員。

Junpeng Lao 是 Google 的數據科學家。在此之前，他在認知神經科學領域完成了博士學位並擔任博士後研究員。在學術生涯中，他主要使用自助法和排列法，但後來對貝葉斯統計和生成建模產生了興趣。