Ankan Mastering Probabilistic Graphical Models with Python

Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python
1. Auflage 2025
ISBN: 978-1-78439-521-6
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 284 Seiten

Key FeaturesBook DescriptionProbabilistic 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 forIf 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.

Autoren/Hrsg.

Ankan, Ankur

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein

Über Autor(innen)

Ankan Ankur :

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.