Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information
This work addresses the challenge of efficiently deriving interpretable Bayesian networks for domain experts, though it appears incremental as it builds on existing methods for network construction.
The researchers tackled the problem of automatically constructing sparse Bayesian networks from unstructured probabilistic models and expert causal information, resulting in an algorithm that incrementally builds networks to maximize conditional independence revelation using a greedy heuristic.
An algorithm for automated construction of a sparse Bayesian network given an unstructured probabilistic model and causal domain information from an expert has been developed and implemented. The goal is to obtain a network that explicitly reveals as much information regarding conditional independence as possible. The network is built incrementally adding one node at a time. The expert's information and a greedy heuristic that tries to keep the number of arcs added at each step to a minimum are used to guide the search for the next node to add. The probabilistic model is a predicate that can answer queries about independencies in the domain. In practice the model can be implemented in various ways. For example, the model could be a statistical independence test operating on empirical data or a deductive prover operating on a set of independence statements about the domain.