I am the author of Building Probabilisitic Graphical Models in Python , published by Packt Publishers.

This is a gentle introduction to the field of Graphical Models, which have applications in Machine Learning, Text Mining and Natural Language Processing.

The source code for the exercises in the book are in the Github repo.

Praise for the book:

“Building Probabilistic Graphical Models (PGMs) with Python” book is an excellent pick up for programmers who just want to know basics of the PGMs and quickly apply them to solve their analytical problems. Book’s author, Kiran has done an excellent work in collecting knowledge about the PGMs from multiple places and providing it in a simple and lucid form.

I would say that this book is one stop buy for anyone who quickly wants to put hands dirty and start using to solve their analytical problems.

• Satnam Singh, Data Scientist

This book is perfect to get you started with probabilistic graphical models (PGM) with Python. It starts with a quick intro to Bayesian and Markov Networks covering concepts like conditional independence and D-separation. It then covers the different aspects of PGM: structure learning, parameter estimation (with frequentist or Bayesian approach) and inference. All is illustrated with examples and code snippets using mostly the libpgm package. PyMC is used for Bayesian parameter estimation.

Definitely an enjoyable read if you're interested in PGM with Python.

-Mr Gramfort Alexandre