Bayesian Modeling and Computation in Python
暫譯: Python中的貝葉斯建模與計算

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

Bayesian Modeling and Computation in Python

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商品描述

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.

商品描述(中文翻譯)

《Python中的貝葉斯建模與計算》旨在幫助初學者的貝葉斯實踐者成為中級建模者。它採用實作方式，使用PyMC3、Tensorflow Probability、ArviZ及其他庫，專注於應用統計的實踐，並參考其背後的數學理論。

本書首先回顧貝葉斯推斷的概念。第二章介紹了貝葉斯模型的探索性分析的現代方法。在理解這兩個基本概念後，隨後的章節將討論各種模型，包括線性回歸、樣條、時間序列、貝葉斯加法回歸樹。最後幾章包括近似貝葉斯計算、端到端案例研究，展示如何在不同環境中應用貝葉斯建模，以及一章關於概率編程語言的內部運作。最後一章作為本書的參考，深入數學方面或擴展某些主題的討論。

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

電子書版本已進行修正和重新排版。如果更新版本尚未發送，請聯繫賣家。

作者簡介

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.

作者簡介(中文翻譯)

Osvaldo A. Martin 是阿根廷 IMASL-CONICET 的研究員，以及芬蘭阿爾托大學計算機科學系的成員。他擁有生物物理學和結構生物資訊學的博士學位。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。他特別受到開發和實現貝葉斯統計和概率建模軟體工具的激勵。

Ravin Kumar 是 Google 的數據科學家，之前曾在 SpaceX 和 sweetgreen 等公司工作。他擁有製造工程碩士學位和機械工程學士學位。他發現貝葉斯統計是一個出色的工具，用於建模組織和指導策略。對靈活統計建模的興趣使他得到了熱情歡迎的開源社群的支持，現在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，他完成了博士學位，並隨後在認知神經科學領域擔任博士後研究員。在學術生涯中，他主要使用自助法（Bootstrapping）和置換法（Permutation）工作，對貝葉斯統計和生成建模產生了濃厚的興趣。

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