Book description
Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python
About This Book
- Gain in-depth knowledge of Probabilistic Graphical Models
- Model time-series problems using Dynamic Bayesian Networks
- A practical guide to help you apply PGMs to real-world 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 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.
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 propagation-based approximation algorithm
- Propagation with approximate messages
- Sampling-based 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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