Mastering Probabilistic Graphical Models Using Python
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Mastering Probabilistic Graphical Models Using Python

Ankur Ankan, Abinash Panda, Abinash Panda

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eBook - ePub

Mastering Probabilistic Graphical Models Using Python

Ankur Ankan, Abinash Panda, Abinash Panda

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About This Book

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 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 real-world examples
  • Deploy probabilistic graphical models 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 concisely represent and optimally predict values in our data problems.

Graphical models gives us techniques to find complex patterns in the data and are widely used in the field of speech recognition, information extraction, image segmentation, and modeling gene regulatory networks.

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 run different inference algorithms on them. There is an entire chapter that goes on to cover Naive Bayes model and Hidden Markov models. These models have been thoroughly discussed using real-world examples.

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Information

Year
2015
ISBN
9781784395216
Edition
1

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
Support files, eBooks, discount offers, and more
Why subscribe?
Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Bayesian Network Fundamentals
Probability theory
Random variable
Independence and conditional independence
Installing tools
IPython
pgmpy
Representing independencies using pgmpy
Representing joint probability distributions using pgmpy
Conditional probability distribution
Representing CPDs using pgmpy
Graph theory
Nodes and edges
Walk, paths, and trails
Bayesian models
Representation
Factorization of a distribution over a network
Implementing Bayesian networks using pgmpy
Bayesian model representation
Reasoning pattern in Bayesian networks
D-separation
Direct connection
Indirect connection
Relating graphs and distributions
IMAP
IMAP to factorization
CPD representations
Deterministic CPDs
Context-specific CPDs
Tree CPD
Rule CPD
Summary
2. Markov Network Fundamentals
Introducing the Markov network
Parameterizing a Markov network – factor
Factor operations
Gibbs distributions and Markov networks
The factor graph
Independencies in Markov networks
Constructing graphs from distributions
Bayesian and Markov networks
Converting Bayesian models into Markov models
Converting Markov models into Bayesian models
Chordal graphs
Summary
3. Inference – Asking Questions to Models
Inference
Complexity of inference
Variable elimination
Analysis of variable elimination
Finding elimination ordering
Using the chordal graph property of induced graphs
Minimum fill/size/weight/search
Belief propagation
Clique tree
Constructing a clique tree
Message passing
Clique tree calibration
Message passing with division
Factor division
Querying variables that are not in the same cluster
MAP inference
MAP using variable elimination
Factor maximization
MAP using belief propagation
Finding the most probable assignment
Predictions from the model using pgmpy
A comparison of variable elimination and belief propagation
Summary
4. Approximate Inference
The optimization problem
The energy function
Exact inference as an optimization
The propagation-based approximation algorithm
Cluster graph belief propagation
Constructing cluster graphs
Pairwise Markov networks
Bethe cluster graph
Propaga...

Table of contents