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Description

Probabilistic Graphical Models is a technique in machine learning that uses the concepts of graph theory to compactly represent and optimally predict values in our data problems. In real world problems, it's often difficult to select the appropriate graphical model as well as the appropriate inference algorithm, which can make a huge difference in computation time and accuracy. Thus, it is crucial to know the working details of these algorithms. This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also to run different inference algorithms on them. There is a complete chapter devoted to the most widely used networks Naive Bayes Model and Hidden Markov Models (HMMs). These models have been thoroughly discussed using real-world examples.

What you will learn

Get to know the basics of probability theory and graph theory

Work with Markov networks

Implement Bayesian networks

Exact inference techniques in graphical models such as the variable elimination algorithm

Understand approximate inference techniques in graphical models such as message passing algorithms

Sampling algorithms in graphical models

Grasp details of Naive Bayes with realworld examples

Deploy probabilistic graphical models using various libraries in Python

Gain working details of Hidden Markov models with realworld examples

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Amazon verified reviews

AlfredO Apr 22, 2017

The subject is covered well and with lots of code examples. I found it very readable even though this was my first contact with PGM

Amazon Verified review

rdasxy Oct 05, 2015

I bought this book while still parallelly working through Daphne Koller's Probabilistic Graphical Models course and textbook, and it was a great resource in helping me understand and apply the concepts using python.Highly recommended!

Ashish K. Oct 30, 2015

I thoroughly enjoyed this book.. its lucid and to-the-point writing really drives home the concepts.I say its a must-have book.great job guys.. No wonder u are from the best engineering college in India..

Anhnhat Tran Apr 26, 2018

The main content of this book is based on the following book "Probabilistic Graphical Models: Principles and Techniques". In multiple places, we can see that this book just summarizes or follows strictly the main points of the above book. The plus side of this book is that it provides more examples, which may help readers understand more deeply about the subject. There are a few typos, which makes it difficult to read. This book can be a good supplement for the above book but can hardly stand on its own, due to lacking of originality.

Roest Sep 23, 2018

What is nice about this book is that it is based on an open source Python library that implements the concepts. Also, it provides a somewhat comprehensive overview of Bayesian and Markov network theory. The downside is that the code fragments in the book are just wrong on more than one occasion, numerical results that are presented are sometimes wrong and more than once, the code fragments are just besides the point. The explanation of the concepts is not the best and the extremely bad layout of formulas in the e-book doesn’t help. So, in summary: nice if you want to try the Python library, but not really brilliant.

Mastering Probabilistic Graphical Models with Python: Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python

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