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Mastering Probabilistic Graphical Models Using Python

ebook

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.


  • Expand title description text
    Publisher: Packt Publishing

    Kindle Book

    • Release date: August 3, 2015

    OverDrive Read

    • ISBN: 9781784395216
    • File size: 13344 KB
    • Release date: August 3, 2015

    EPUB ebook

    • ISBN: 9781784395216
    • File size: 13344 KB
    • Release date: August 3, 2015

    PDF ebook

    • ISBN: 9781784395216
    • File size: 3309 KB
    • Release date: August 3, 2015

    Formats

    Kindle Book
    OverDrive Read
    EPUB ebook
    PDF ebook

    Languages

    English

    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.


  • Expand title description text
    • Details

      Publisher:
      Packt Publishing

      Kindle Book
      Release date: August 3, 2015

      OverDrive Read
      ISBN: 9781784395216
      File size: 13344 KB
      Release date: August 3, 2015

      EPUB ebook
      ISBN: 9781784395216
      File size: 13344 KB
      Release date: August 3, 2015

      PDF ebook
      ISBN: 9781784395216
      File size: 3309 KB
      Release date: August 3, 2015

    • Creators
    • Formats
      Kindle Book
      OverDrive Read
      EPUB ebook
      PDF ebook
    • Languages
      English