Approximation methods for efficient learning of Bayesian networks / Carsten Riggelsen.

Format:

Book

Thesis/Dissertation

Author/Creator:

Riggelsen, Carsten.

Series:

Frontiers in artificial intelligence and applications. Dissertations in artificial intelligence.

Frontiers in artificial intelligence and applications ; v. 168.

Frontiers in artificial intelligence and applications ; v. 168

Dissertations in artificial intelligence

Language:

English

Subjects (All):

Bayesian statistical decision theory.

Machine learning.

Neural networks (Computer science).

Physical Description:

1 online resource (148 p.)

Edition:

1st ed.

Place of Publication:

Amsterdam ; Washington, DC : IOS Press, c2008.

Language Note:

English

Summary:

This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order t

Contents:

Title page; Contents; Foreword; Introduction; Preliminaries; Learning Bayesian Networks from Data; Monte Carlo Methods and MCMC Simulation; Learning from Incomplete Data; Conclusion; References

Notes:

Description based upon print version of record.

Thesis (Ph.D.)--Utrecht University, 2006.

Includes bibliographical references (p. [133]-137).

ISBN:

6611733337

1-281-73333-4

9786611733339

1-60750-298-4

600-00-0346-3

1-4337-1131-1

OCLC:

437202842