Modeling and reasoning with Bayesian networks / Adnan Darwiche.

Format:

Book

Author/Creator:

Darwiche, Adnan, 1966- author.

Language:

English

Subjects (All):

Bayesian statistical decision theory--Graphic methods.

Bayesian statistical decision theory.

Inference.

Probabilities.

Modeling.

Physical Description:

1 online resource (xii, 548 pages) : digital, PDF file(s).

Other Title:

Modeling & Reasoning with Bayesian Networks

Place of Publication:

Cambridge : Cambridge University Press, 2009.

Language Note:

English

Summary:

This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Contents:

Introduction

Propositional logic

Probability calculus

Bayesian networks

Building Bayesian networks

Inference by variable elimination

Inference by factor elimination

Inference by conditioning

Models for graph decomposition

Most likely instantiations

The complexity of probabilistic inference

Compiling Bayesian networks

Inference with local structure

Approximate inference by belief propagation

Approximate inference by stochastic sampling

Sensitivity analysis

Learning : the maximum likelihood approach

Learning : the Bayesian approach.

Notes:

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Includes bibliographical references and index.

ISBN:

1-107-20035-0

1-139-63770-3

1-283-33017-2

9786613330178

1-139-13468-X

1-139-12963-5

1-139-13356-X

0-511-50514-0

0-511-81135-7

0-511-50728-3

OCLC:

476264610