Bayesian Analysis with Python : A practical guide to probabilistic modeling by Osvaldo Martin

Dive into the world of Bayesian modeling with "Bayesian Analysis with Python - Third Edition." This comprehensive guide introduces you to the fundamental concepts of Bayesian analysis using Python libraries like PyMC, ArviZ, Bambi, and more. Led by Osvaldo Martin, an experienced Bayesian modeler, you'll embark on a journey through step-by-step guidance, modern methodologies, and practical exercises designed to enhance your understanding.

Key Features:
- Master Bayesian data analysis through hands-on tutorials
- Utilize state-of-the-art Python libraries for probabilistic modeling
- Benefit from sample problems and exercises to reinforce learning
- Access a free PDF eBook with the purchase of the print or Kindle edition

Book Description:
In the third edition, Osvaldo Martin offers an updated approach to Bayesian modeling, incorporating new topics like Bayesian additive regression trees (BART) and featuring refined explanations based on user feedback. From probability theory basics to advanced hierarchical models and Gaussian processes, you'll explore various models using both synthetic and real-world datasets. By the end, you'll possess the skills to design, implement, and interpret Bayesian models for a range of data science challenges.

What You Will Learn:
- Build probabilistic models using PyMC and Bambi
- Analyze and interpret models with ArviZ
- Conduct prior and posterior predictive checks
- Explore hierarchical models and model comparison techniques
- Apply Bayesian frameworks to real-world problems

Who This Book Is For:
Whether you're a student, data scientist, researcher, or developer, this book serves as your gateway to Bayesian data analysis and probabilistic programming. No prior statistical knowledge is required, making it accessible to beginners, although familiarity with Python and scientific libraries like NumPy is recommended.

Table of Contents:
1. Thinking Probabilistically
2. Programming Probabilistically
3. Hierarchical Models
4. Modeling with Lines
5. Comparing Models
6. Modeling with Bambi
7. Mixture Models
8. Gaussian Processes
9. Bayesian Additive Regression Trees
10. Inference Engines
11. Where to Go Next

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