Mastering Probabilistic Graphical Models Using Python

Mastering Probabilistic Graphical Models Using Python
Author :
Publisher : Packt Publishing Ltd
Total Pages : 284
Release :
ISBN-10 : 9781784395216
ISBN-13 : 1784395218
Rating : 4/5 (218 Downloads)

Book Synopsis Mastering Probabilistic Graphical Models Using Python by : Ankur Ankan

Download or read book Mastering Probabilistic Graphical Models Using Python written by Ankur Ankan and published by Packt Publishing Ltd. This book was released on 2015-08-03 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


Mastering Probabilistic Graphical Models Using Python Related Books

Building Probabilistic Graphical Models with Python
Language: en
Pages: 172
Authors: Kiran R. Karkera
Categories: Graphical modeling (Statistics)
Type: BOOK - Published: 2014 - Publisher:

DOWNLOAD EBOOK

"This is a short, practical guide that allows data scientists to understand the concepts of Graphical models and enables them to try them out using small Python
Mastering Probabilistic Graphical Models Using Python
Language: en
Pages: 284
Authors: Ankur Ankan
Categories: Computers
Type: BOOK - Published: 2015-08-03 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python About This Book Gain in-depth knowledge o
Probabilistic Graphical Models
Language: en
Pages: 1270
Authors: Daphne Koller
Categories: Computers
Type: BOOK - Published: 2009-07-31 - Publisher: MIT Press

DOWNLOAD EBOOK

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making deci
Probabilistic Graphical Models
Language: en
Pages: 370
Authors: Luis Enrique Sucar
Categories: Computers
Type: BOOK - Published: 2020-12-23 - Publisher: Springer Nature

DOWNLOAD EBOOK

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engi
Building Probabilistic Graphical Models with Python
Language: en
Pages: 172
Authors: Kiran K. Karkera
Categories:
Type: BOOK - Published: 2015-05-18 - Publisher: CreateSpace

DOWNLOAD EBOOK

With the increasing prominence in machine learning and data science applications, probabilistic graphical models are a new tool that machine learning users can