Book description
Solve machine learning problems using probabilistic graphical models implemented in Python with realworld applications
In Detail
With the increasing prominence in machine learning and data science applications, probabilistic graphical models are a new tool that machine learning users can use to discover and analyze structures in complex problems. The variety of tools and algorithms under the PGM framework extend to many domains such as natural language processing, speech processing, image processing, and disease diagnosis.
You've probably heard of graphical models before, and you're keen to try out new landscapes in the machine learning area. This book gives you enough background information to get started on graphical models, while keeping the math to a minimum.
What You Will Learn
 Create Bayesian networks and make inferences
 Learn the structure of causal Bayesian networks from data
 Gain an insight on algorithms that run inference
 Explore parameter estimation in Bayes nets with PyMC sampling
 Understand the complexity of running inference algorithms in Bayes networks
 Discover why graphical models can trump powerful classifiers in certain problems
Publisher resources
Table of contents

Building Probabilistic Graphical Models with Python
 Table of Contents
 Building Probabilistic Graphical Models with Python
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Preface
 1. Probability
 2. Directed Graphical Models
 3. Undirected Graphical Models
 4. Structure Learning

5. Parameter Learning
 The likelihood function
 Parameter learning example using MLE
 MLE for Bayesian networks
 Bayesian parameter learning example using MLE
 Data fragmentation
 Effects of data fragmentation on parameter estimation
 Bayesian parameter estimation
 Bayesian estimation for the Bayesian network
 Example of Bayesian estimation
 Summary
 6. Exact Inference Using Graphical Models
 7. Approximate Inference Methods
 A. References
 Index
Product information
 Title: Building Probabilistic Graphical Models with Python
 Author(s):
 Release date: June 2014
 Publisher(s): Packt Publishing
 ISBN: 9781783289004
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