Introduction to Bayesian Analysis in Python

Video Description

This course focuses on the application of relevant Bayesian techniques

About This Video

  • Simplify the Bayes process to solve complex statistical problems using Python
  • Tutorial guide that will take the 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

In Detail

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.

All the codes of the course are uploaded on the Github repository:

Product Information

  • Title: Introduction to Bayesian Analysis in Python
  • Author(s): Sunil Gupta
  • Release date: December 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781788997010