Advances in Bayesian Networks pp 201-216 | Cite as
A Graphical Meta-Model for Reasoning about Bayesian Network Structure
Abstract
When the amount of available data is small with respect to the problem size, many Bayesian networks can account for the data in a similar way. In these cases model averaging offers a framework which allows us to make better predictions than model selection. Selective model averaging directly uses a subset of these networks with high probability mass to reason over the probability of the structural features (arcs) which can appear in the problem domain network model. In this paper, instead of using this subset of networks to reason about the domain network structure, we propose to use the probability distribution over the structural features induced by these networks, in order to learn a new Bayesian network whose variables are the structural features which can appear in the problem domain network. This network can be considered as a higher level model or meta model, because it can be seen as an approximation of the whole space of Bayesian networks defined over the problem domain variables. This meta-model (Bayesian network) can be used to reason about the probability of structural features, as selective model averaging, but also as a decision support tool to be used by an expert in the problem domain.
Keywords
Bayesian Network Directed Acyclic Graph Problem Domain Variable Neighbourhood Search Markov Chain Monte Carlo MethodPreview
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