Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information
Sampath Srinivas, Stuart Russell, Alice Agogino
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.
PDF Link: /papers/89/p295-srinivas.pdf
AUTHOR = "Sampath Srinivas
and Stuart Russell and Alice Agogino",
TITLE = "Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information",
BOOKTITLE = "Uncertainty in Artificial Intelligence 5 Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1989",
PAGES = "295--308"