Skip to main content
Have a personal or library account? Click to login
Mastering Probabilistic Graphical Models with Python Cover

Mastering Probabilistic Graphical Models with Python

Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python

Publisher:Packt Publishing Limited
Paid access
|Oct 2025
Product purchase options
Description

Key Features

    Book Description

    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
    • Sampling algorithms in graphical models
    • Grasp details of Naive Bayes with realworld examples
    • Deploy probabilistic graphical models using various libraries in Python
    • Gain working details of Hidden Markov models with realworld 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 them in your data science problems.

    Table of Contents

    1. Bayesian Network Fundamentals
    2. Markov Network Fundamentals
    3. Inference: Asking Questions to Models
    4. Approximate Inference Methods: Sampling
    5. Model Learning: Parameter Estimation in Bayesian Networks
    6. Model Learning: Parameter Estimation in Markov Networks
    7. Specialized Models
    PDF ISBN: 978-1-78439-521-6
    Publisher: Packt Publishing Limited
    Copyright owner: © 2015 Packt Publishing Limited
    Publication date: 2025
    Language: English
    Pages: 284
    Related subjects:
    General interest

    People also read

    Learning Probabilistic Graphical Models in R
    Book

    Learning Probabilistic Graphical Models in R

    Bayesian Analysis with Python.
    Book

    Bayesian Analysis with Python.

    Osvaldo Martin
    Bayesian Analysis with Python
    Book

    Bayesian Analysis with Python

    Osvaldo Martin, Christopher Fonnesbeck, Thomas Wiecki, Christopher Fonnesbeck, Thomas Wiecki
    Bayesian Analysis with Python
    Book

    Bayesian Analysis with Python

    Learning Bayesian Models with R
    Book
    Coming soon

    Learning Bayesian Models with R

    Hands-On Markov Models with Python
    Book

    Hands-On Markov Models with Python

    Ankur Ankan, Abinash Panda
    R概率图模型入门与实践
    Book

    R概率图模型入门与实践

    Posts & Telecom Press, David Bellot
    Graph Machine Learning
    Book

    Graph Machine Learning

    Aldo Marzullo, Enrico Deusebio, Claudio Stamile
    Enhancing Deep Learning with Bayesian Inference
    Book

    Enhancing Deep Learning with Bayesian Inference

    Matt Benatan, Jochem Gietema, Marian Schneider
    Pragmatic Machine Learning with Python
    Book

    Pragmatic Machine Learning with Python

    Avishek Nag