Bayesian Analysis with Python, 3rd Edition
Bayesian Analysis with Python is a practical introduction to applied Bayesian modeling and probabilistic programming. This third edition covers PyMC and related tools through examples and exercises, with topics including hierarchical models, regression, mixture models, Gaussian processes, and Bayesian additive regression trees.
At a glance
- Introduces applied Bayesian modeling with PyMC and related Python tools, including ArviZ, Bambi, PreliZ, PyMC-BART, and Kulprit.
- Covers hierarchical and generalized linear models, mixture models, Gaussian processes, and Bayesian additive regression trees.
- Includes synthetic and real datasets, sample problems, and practice exercises.
- Topics include prior and posterior predictive checks, model comparison, and interpreting results.
Reference price: $49.99
Price checked: Nov 14, 2026
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Pros and cons
Pros
- Broad coverage connects core Bayesian concepts with workflows across several Python libraries.
- Exercises and examples provide opportunities to apply the ideas rather than relying only on theory.
- The listing says print and Kindle purchases include a free PDF eBook.
Technical specifications
9 specifications| Edition | 3rd |
|---|---|
| Publisher | Packt Publishing |
| Publication date | January 31, 2024 |
| Language | English |
| Print length | 394 pages |
| ISBN-10 | 1805127160 |
| ISBN-13 | 978-1805127161 |
| Weight listed | 1.49 pounds |
|---|---|
| Dimensions | 7.5 x 0.89 x 9.25 inches |
What to know before buying
- The listing says access to the DRM-free PDF and Packt Reader requires email sign-up and proof of purchase. Some prior Python experience and scientific libraries such as NumPy are expected, though no previous statistical knowledge is required.
Who is it for?
A good fit for
- Students, data scientists, researchers, and developers starting Bayesian data analysis with Python.
- Python users who want an applied introduction to probabilistic programming and modern Bayesian modeling tools.
Less suited to
- Readers looking for a Python-free treatment or a book that assumes no programming experience.
Customer reviews on Amazon
- 5 stars
79% 79% - 4 stars
15% 15% - 3 stars
6% 6% - 2 stars
0% 0% - 1 star
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Aggregate ratings lean positive, with most ratings in the five-star category and a smaller share in four- and three-star categories. No written feedback is supplied, so the ratings do not reveal which topics or features readers valued or where they encountered difficulty. For buyers considering this Python-focused introduction, the listed coverage and exercises are useful selection criteria, but the rating data alone cannot establish how well the material fits a particular reader.
Frequently asked questions
What does the third edition cover?
It covers applied Bayesian modeling with PyMC and related libraries, including hierarchical models, regression, mixture models, Gaussian processes, and Bayesian additive regression trees.
Is prior statistics knowledge required?
The listing describes the book as introductory and says previous statistical knowledge is not required. It does expect some Python experience and familiarity with scientific libraries such as NumPy.
Does a print or Kindle purchase include a PDF?
The listing says print and Kindle purchases include a free PDF eBook. Email sign-up and proof of purchase are required to access the PDF and Packt Reader.
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