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

TL;DR

This paper presents an algorithm for automatically constructing sparse Bayesian networks from unstructured probabilistic models and expert domain knowledge, emphasizing the explicit representation of conditional independencies.

Contribution

It introduces a novel incremental method guided by expert input and heuristics to efficiently build interpretable Bayesian networks from various probabilistic models.

Findings

Successfully constructs sparse Bayesian networks with explicit independence information

Uses a greedy heuristic to minimize added arcs during network construction

Applicable to models based on statistical tests or deductive independence statements

Abstract

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.