Mastering Probabilistic Graphical Models using Python

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.

Who is this book for?
If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian learning or probabilistic graphical models, this book will help you to understand the details of graphical models and use them in your data science problems.

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 exam