Mastering Probabilistic Graphical Models using Python

Ankur Ankan and Abinash Panda

Published by Packt Publishing - ebooks Account, 2015
ISBN 10: 1784394688 / ISBN 13: 9781784394684
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Synopsis:

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 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.

About the Author:

Ankur Ankan

Ankur Ankan is a BTech graduate from IIT (BHU), Varanasi. He is currently working in the field of data science. He is an open source enthusiast and his major work includes starting pgmpy with four other members. In his free time, he likes to participate in Kaggle competitions.



Abinash Panda

Abinash Panda is an undergraduate from IIT (BHU), Varanasi, and is currently working as a data scientist. He has been a contributor to open source libraries such as the Shogun machine learning toolbox and pgmpy, which he started writing along with four other members. He spends most of his free time on improving pgmpy and helping new contributors.

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Bibliographic Details

Title: Mastering Probabilistic Graphical Models ...
Publisher: Packt Publishing - ebooks Account
Publication Date: 2015
Binding: Paperback
Book Condition: Used: Good

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Book Description Packt Publishing Limited, United Kingdom, 2015. Paperback. Book Condition: New. Language: English . Brand New Book ***** Print on Demand *****.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 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. 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 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. 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. Bookseller Inventory # AAV9781784394684

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Book Description Packt Publishing Limited, United Kingdom, 2015. Paperback. Book Condition: New. Language: English . Brand New Book ***** Print on Demand *****. 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 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. 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 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. 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. Bookseller Inventory # AAV9781784394684

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Book Description Packt Publishing - ebooks Account. Paperback. Book Condition: New. Paperback. 287 pages. 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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Bookseller Inventory # 9781784394684

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Book Description Packt Publishing Limited, United Kingdom, 2015. Paperback. Book Condition: New. Language: English . This book usually ship within 10-15 business days and we will endeavor to dispatch orders quicker than this where possible. Brand New Book. 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 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. 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 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. 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. Bookseller Inventory # LIE9781784394684

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