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Bayesian Analysis with Python: A practical guide to probabilistic modeling

Brand: Osvaldo Martin

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Bayesian Analysis with Python: A practical guide to probabilistic modeling

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Best Sellers Rank: #133,021 in Books (See Top 100 in Books) #11 in Mathematical & Statistical Software #78 in Probability & Statistics (Books) #105 in Python Programming
Customer Reviews: 4.6 out of 5 stars 34Reviews

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From the Publisher

How has your experience in Bayesian analysis helped you write this book? 

Through my work at The National Scientific and Technical Research Council (CONICET) in Argentina and my experience collaborating with global researchers and developers, I have acquired an excellent understanding of the intricacies of computational methods for Bayesian statistics and probabilistic programming. My involvement in various open-source projects, including ArviZ, Bambi, Kulprit, PreliZ, and PyMC, moreover, has enabled me to incorporate practical, real-world applications of Bayesian analysis, enriching the content and ensuring its relevance to contemporary research and industry needs. With my prior teaching experience, I have been able to craft the book to cater to readers who are new to the field, fostering a learning experience that promotes a deeper understanding of the subject matter.

What is new in this edition?

In this latest edition, I’ve made significant updates and improvements to enhance the learning experience for readers. This edition extensively incorporates the latest versions of PyMC and ArviZ, emphasizing their newest and most advanced features. Additionally, four new libraries from the PyMC ecosystem—Bambi, Kulrprit, PreliZ, and PyMC-BART—have been introduced, significantly expanding the book's scope. I’ve also included dedicated chapters to offer practical insights and real-world applications specifically for Bambi and PyMC-BART, allowing readers to delve deeper into these libraries and apply them effectively in various scenarios.

What makes this book different from other Bayesian Analysis titles?

I’ve written this book with the emphasis on prioritizing practical application and conceptual understanding over a purely mathematical approach. By including both synthetic and real-world examples, I’ve attempted to enrich the learning experience, using synthetic cases to explain concepts and real examples to demonstrate practical applications. This way, the book promotes active engagement through exercises, fostering a hands-on learning experience for Python enthusiasts eager to master Bayesian analysis.

	Bayesian Analysis with Python - Third Edition	Bayesian Analysis with Python - Second Edition	Pandas Cookbook - Third Edition

Customer Reviews	34	52	32
Price	$35.56	$46.13	$39.72
Description	Introduces Bayesian analysis with PyMC and ArviZ, and four new libraries from the PyMC ecosystem, offering practical insights and real-world applications	Introduces Bayesian statistics with enhanced practicality, utilizing PyMC3 and ArviZ to master various models, from hierarchical to Gaussian processes	Master pandas 2.x with practical recipes for structured data manipulation, analysis, and performance tuning
Libraries	PyMC, ArviZ, Bambi, PreliZ, Kulprit, and PyMC-BART	PyMC3 and ArviZ	pandas, NumPy, PyArrow, Jupyter Notebook
Topics	Bayesian additive regression trees (BART), non-parametric regression, variable selection, and prior elicitation	Generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes	Structured data, data manipulation, I/O, performance, idiomatic pandas, visualization, time series

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Bayesian Analysis with Python: A practical guide to probabilistic modeling

From the Publisher

How has your experience in Bayesian analysis helped you write this book? ​

What is new in this edition?

What makes this book different from other Bayesian Analysis titles?​

How has your experience in Bayesian analysis helped you write this book? 

What makes this book different from other Bayesian Analysis titles?