Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, 2nd Edition

Osvaldo Martin




An introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ

Key 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.
Code and figures
You canfind the code and figures in this GitHub repository You can also use this repository to reportany problem you find with the book or code

Book Description

The second edition of Bayesian Analysis with Python is an introductionto the main concepts of applied Bayesian inference and its practicalimplementation in Python using PyMC3, a state-of-the-art probabilisticprogramming library, and ArviZ a new library for exploratory analysis of Bayesian models.

    The main concepts of Bayesian statisticsare covered using a practical and computational approach. Synthetic andreal data sets are used to introduce several types of models such asgeneralized linear models for regression and classification, mixturemodels, hierarchical models and Gaussian process among others.

By the end of the book, you will have a working knowledge ofprobabilistic modeling and you will be able to design and implementBayesian models for your own data science problems. After reading thebook you will be better prepared to delve into more advance material orspecialized statistical modeling in case you need it.

What you will 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 under a probabilistic perspective
  • Think probabilistically and benefit from the flexibility of the Bayesian framework

Who This Book Is For

If you are a student, data scientist, researcher in the natural orsocial sciences, or a developer looking to get started with Bayesiandata analysis and probabilistic programming, this book is for you. Thebook is introductory so no previous statistical knowledge is required,although some experience in using Python and NumPy is expected.

Table of Contents
  1. Thinking Probabilistically
  2. Programming Probabilistically
  3. Modeling with Linear Regression
  4. Generalizing Linear Models
  5. Model Comparison
  6. Mixture Models
  7. Gaussian Processes
  8. Inference Engines
  9. Where to go next?