A Bayesian Method for Constructing Bayesian Belief Networks from Databases

TL;DR

This paper introduces a Bayesian approach for building Bayesian belief networks directly from databases, aiming to facilitate scientific discovery and expert system development.

Contribution

It presents a novel Bayesian algorithm for constructing belief networks from case databases, expanding automated probabilistic modeling capabilities.

Findings

Preliminary evaluation shows promising results in network construction accuracy.

The method relates to prior work and discusses open research problems.

Abstract

This paper presents a Bayesian method for constructing Bayesian belief networks from a database of cases. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. We relate the methods in this paper to previous work, and we discuss open problems.