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Building Probabilistic Graphical Models with Python

Kiran R Karkera

Solve machine learning problems using probabilistic graphical models implemented in Python, with real-world applications
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Book Details

ISBN 139781783289004
Paperback172 pages

About This Book

  • Stretch the limits of machine learning by learning how graphical models provide an insight on particular problems, especially in high dimension areas such as image processing and NLP
  • Solve real-world problems using Python libraries to run inferences using graphical models
  • A practical, step-by-step guide that introduces readers to representation, inference, and learning using Python libraries best suited to each task

Who This Book Is For

If you are a data scientist who knows about machine learning and want to enhance your knowledge of graphical models, such as Bayes network, in order to use them to solve real-world problems using Python libraries, this book is for you.This book is intended for those who have some Python and machine learning experience, or are exploring the machine learning field.

Table of Contents

Chapter 1: Probability
The theory of probability
Goals of probabilistic inference
Conditional probability
The chain rule
The Bayes rule
Interpretations of probability
Random variables
Marginal distribution
Joint distribution
Conditional independence
Types of queries
Chapter 2: Directed Graphical Models
Graph terminology
Independence and independent parameters
The Bayes network
Reasoning patterns
Factorization and I-maps
The Naive Bayes model
Chapter 3: Undirected Graphical Models
Pairwise Markov networks
The Gibbs distribution
An induced Markov network
Flow of influence
Active trail and separation
Structured prediction
The factorization-independence tango
Chapter 4: Structure Learning
The structure learning landscape
Constraint-based structure learning
Score-based learning
Chapter 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
Chapter 6: Exact Inference Using Graphical Models
Complexity of inference
Using the Variable Elimination algorithm
The tree algorithm
Chapter 7: Approximate Inference Methods
The optimization perspective
Steps in the LBP algorithm
Sampling-based methods

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

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.


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