Mastering probabilistic graphical models using Python

Mastering probabilistic graphical models using Python: master probablistic graphical models by learning through real-world problems and illustrative code examples in Python

by Ankan, Ankur

Type Book
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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.

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Title Statements

  • Full title :Mastering probabilistic graphical models using Python : master probablistic graphical models by learning through real-world problems and illustrative code examples in Python / Ankur Ankan, Abinash Panda.

Summary

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.

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Edition Statements

  • Publication Date:2015.

Publication Statements

  • Publisher(s):Packt Publishing
  • Place of Publication:Birmingham, UK
  • Language:eng
  • Publication Date:2015.

Notes

Includes index.

The Physical Object

  • Description:1 online resource (1 volume) : : illustrations.

Edition Identifiers

  • ISBN:9781784395216, 1784395218, 1784394688, 9781784394684
  • oclc:ocn922580777
  • LC Classification:QA279
  • Dewey Decimal:519.5

Table of Contents

Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Bayesian Network Fundamentals; Probability theory; Random variable; Independence and conditional independence; Installing tools; IPython; pgmpy; Representing independencies using pgmpy; Representing joint probability distributions using pgmpy; Conditional probability distribution; Representing CPDs using pgmpy; Graph theory; Nodes and edges; Walk, paths, and trails; Bayesian models; Representation; Factorization of a distribution over a network

Implementing Bayesian networks using pgmpyBayesian model representation; Reasoning pattern in Bayesian networks; D-separation; Direct connection; Indirect connection; Relating graphs and distributions; IMAP; IMAP to factorization; CPD representations; Deterministic CPDs; Context-specific CPDs; Tree CPD; Rule CPD; Summary; Chapter 2: Markov Network Fundamentals; Introducing the Markov network; Parameterizing a Markov network -- factor; Factor operations; Gibbs distributions and Markov networks; The factor graph; Independencies in Markov networks; Constructing graphs from distributions

Bayesian networks and Markov netw…

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Subjects

Graphical modeling (Statistics) Modèles graphiques (Statistique) Python (Computer program language) Python (Langage de programmation)

Writers

Ankan, Ankur Panda, Abinash

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