6533b822fe1ef96bd127cb09

RESEARCH PRODUCT

Learning Bayesian Metanetworks from Data with Multilevel Uncertainty

Vagan TerziyanOleksandra Vitko

subject

business.industryComputer scienceTheoryofComputation_GENERALBayesian networkBayesian inferenceMachine learningcomputer.software_genreVariable-order Bayesian networkBayesian statisticsComputingMethodologies_PATTERNRECOGNITIONBayesian hierarchical modelingBayesian programmingGraphical modelArtificial intelligencebusinesscomputerDynamic Bayesian network

description

Managing knowledge by maintaining it according to dynamic context is among the basic abilities of a knowledge-based system. The two main challenges in managing context in Bayesian networks are the introduction of contextual (in)dependence and Bayesian multinets. We are presenting one possible implementation of a context sensitive Bayesian multinet-the Bayesian Metanetwork, which implies that interoperability between component Bayesian networks (valid in different contexts) can be also modelled by another Bayesian network. The general concepts and two kinds of such Metanetwork models are considered. The main focus of this paper is learning procedure for Bayesian Metanetworks.

yearjournalcountryeditionlanguage
2006-03-07
https://doi.org/10.1007/1-4020-8151-0_17