Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information

• Main Authors: Srinivas, Sampath, Russell, Stuart, Agogino, Alice M
• Format: Journal Article
• Language: English
• Published: 27.03.2013
• Subjects: Computer Science - Artificial Intelligence
• DOI: 10.48550/arxiv.1304.1530

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.