Learn

Build probabilistic models using the Python library PyMC3

Analyze probabilistic models with the help of ArviZ

Acquire the skills required to sanity check models and modify them if necessary

Understand the advantages and caveats of hierarchical models

Find out how different models can be used to answer different data analysis questions

Compare models and choose between alternative ones

Discover how different models are unified from a probabilistic perspective

Think probabilistically and benefit from the flexibility of the Bayesian framework

About

The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.

The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others.

By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.

Features

A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ

A modern, practical and computational approach to Bayesian statistical modeling

A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.