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

Description

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
• Sample algorithms in Graphical Models
• Grasp details of Naive Bayes with real-world examples
• Deploy PGMs using various libraries in Python
• Gain working details of Hidden Markov Models with real-world examples

In Detail

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.

Style and approach

An easy-to-follow guide to help you understand Probabilistic Graphical Models using simple examples and numerous code examples, with an emphasis on more widely used models.