Anzeige
Erschienen in:
2005 | OriginalPaper | Buchkapitel
MCMC Learning of Bayesian Network Models by Markov Blanket Decomposition
verfasst von : Carsten Riggelsen
Erschienen in: Machine Learning: ECML 2005
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We propose a Bayesian method for learning Bayesian network models using Markov chain Monte Carlo (MCMC). In contrast to most existing MCMC approaches that define components in term of single edges, our approach is to decompose a Bayesian network model in larger
dependence
components defined by Markov blankets. The idea is based on the fact that MCMC performs significantly better when choosing the right decomposition, and that edges in the Markov blanket of the vertices form a natural dependence relationship. Using the ALARM and Insurance networks, we show that this decomposition allows MCMC to mix more rapidly, and is less prone to getting stuck in local maxima compared to the single edge approach.