Introduction to Bayesian Analysis in Python

Introduction to Bayesian Analysis in Python

Updated Oct 24, 2019

Bayesian statistics is an effective tool for solving some inference problems when the available sample is too small for more complex statistical analysis to be applied. This course teaches the main concepts of Bayesian data analysis. It focuses on how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, model checking, and validation.

The course introduces the framework of Bayesian Analysis. Complex mathematical theory will be sidestepped in favor of a more pragmatic approach, featuring computational methods implemented in the Python library PyMC3. We present several instances of analysis scenarios. 

Target Audience

Students, researchers, and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to day projects. Programming experience with Python is essential. No previous statistical knowledge is assumed.  

Business Outcomes

  • Simplify the Bayes process to solve complex statistical problems using Python

  • Tutorial guide that will take you through the journey to Bayesian analysis with the help of sample problems and practice exercises

  • Learn how and when to use Bayesian analysis in your applications with this guide