Book description
Master probabilistic graphical models by learning through realworld problems and illustrative code examples in Python
About This Book
 Gain indepth knowledge of Probabilistic Graphical Models
 Model timeseries problems using Dynamic Bayesian Networks
 A practical guide to help you apply PGMs to realworld problems
Who This Book Is 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 it in your data science problems. This book will also help you select the appropriate model as well as the appropriate algorithm for your problem.
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 realworld examples
 Deploy PGMs using various libraries in Python
 Gain working details of Hidden Markov Models with realworld 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 realworld examples.
Style and approach
An easytofollow guide to help you understand Probabilistic Graphical Models using simple examples and numerous code examples, with an emphasis on more widely used models.
Publisher resources
Table of contents

Mastering Probabilistic Graphical Models Using Python
 Table of Contents
 Mastering Probabilistic Graphical Models Using Python
 Credits
 About the Authors
 About the Reviewers
 www.PacktPub.com
 Preface
 1. Bayesian Network Fundamentals
 2. Markov Network Fundamentals
 3. Inference – Asking Questions to Models

4. Approximate Inference
 The optimization problem
 The energy function
 Exact inference as an optimization
 The propagationbased approximation algorithm
 Propagation with approximate messages
 Samplingbased approximate methods
 Forward sampling
 Conditional probability distribution
 Likelihood weighting and importance sampling
 Importance sampling
 Importance sampling in Bayesian networks
 Markov chain Monte Carlo methods
 Gibbs sampling
 The multiple transitioning model
 Using a Markov chain
 Collapsed particles
 Collapsed importance sampling
 Summary
 5. Model Learning – Parameter Estimation in Bayesian Networks
 6. Model Learning – Parameter Estimation in Markov Networks
 7. Specialized Models
 Index
Product information
 Title: Mastering Probabilistic Graphical Models Using Python
 Author(s):
 Release date: August 2015
 Publisher(s): Packt Publishing
 ISBN: 9781784394684
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