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

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.

Table of Contents

Thinking Probabilistically

Programming Probabilistically

Hierarchical Models

Modeling with Lines

Comparing Models

Modeling with Bambi

Mixture Models

Gaussian Processes

Bayesian Additive Regression Trees

Inference Engines

Where to Go Next

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