Bayesian Analysis with Python - Third Edition: A practical guide to probabilistic modeling

Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries

Key Features:

• Conduct Bayesian data analysis with step-by-step guidance
• Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling
• Enhance your learning with best practices through sample problems and practice exercises
• Purchase of the print or Kindle book includes a free PDF eBook.

Book Description:

The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.

In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.

By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.

What You Will Learn:

• Build probabilistic models using PyMC and Bambi
• Analyze and interpret probabilistic models with ArviZ
• Acquire the skills to sanity-check models and modify them if necessary
• Build better models with prior and posterior predictive checks
• Learn the advantages and caveats of hierarchical models
• Compare models and choose between alternative ones
• Interpret results and apply your knowledge to real-world problems
• Explore common models from a unified probabilistic perspective
• Apply the Bayesian framework's flexibility for probabilistic thinking

Who this book is for:

If you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.

學習使用最先進的Python庫（如PyMC、ArviZ、Bambi等）掌握貝葉斯建模的基礎，由一位有經驗的貝葉斯模型師指導，並為這些庫做出貢獻。

主要特點：
- 逐步指導進行貝葉斯數據分析
- 了解現代、實用和計算方法的貝葉斯統計建模
- 通過示例問題和練習提升學習效果
- 購買印刷版或Kindle電子書，可獲得免費PDF電子書。

書籍描述：
《Python貝葉斯分析》第三版是一本介紹應用貝葉斯建模的主要概念的書籍，使用了PyMC這個先進的概率編程庫，以及其他支持和促進建模的庫，如ArviZ（用於貝葉斯模型的探索性分析）、Bambi（用於靈活且易於使用的階層線性建模）、PreliZ（用於先驗引導）、PyMC-BART（用於靈活的非參數回歸）和Kulprit（用於變量選擇）。

在這本更新的第三版中，概率理論的簡要概念介紹增強了學習過程，引入了新的主題，如貝葉斯添加回歸樹（BART），並提供了更新的示例。通過前幾版的反饋和經驗，修正了解釋，強調了書籍對貝葉斯統計的重視。您將使用合成和真實數據集探索各種模型，包括階層模型、回歸和分類的廣義線性模型、混合模型、高斯過程和BART。

通過閱讀本書，您將具備對概率建模的實用理解，能夠為數據科學挑戰設計和實施貝葉斯模型。如果需要，您將為深入研究或專門的統計建模做好充分準備。

學到的內容：
- 使用PyMC和Bambi構建概率模型
- 使用ArviZ分析和解釋概率模型
- 獲得檢查模型的技能，並在必要時進行修改
- 使用先驗和後驗預測檢查來改進模型
- 了解階層模型的優點和注意事項
- 比較模型並選擇替代模型
- 解釋結果並將知識應用於實際問題
- 從統一的概率角度探索常見模型
- 應用貝葉斯框架的靈活性進行概率思維

適合閱讀對象：
如果您是學生、數據科學家、研究人員或開發人員，並希望開始進行貝葉斯數據分析和概率編程，那麼這本書適合您。本書是入門級的，因此不需要先前的統計知識，但需要一些使用Python和科學庫（如NumPy）的經驗。