Refinement and Coarsening of Bayesian Networks

TL;DR

This paper introduces methods for dynamically refining and coarsening state spaces in Bayesian Networks to improve computational efficiency and assessment quality, addressing a gap in existing uncertainty calculi.

Contribution

It presents the first operations for refining and coarsening state spaces specifically within Bayesian Network technology.

Findings

Methods enable dynamic adjustment of state spaces during assessment.

Practical implications for knowledge acquisition are discussed.

Enhances computational efficiency and assessment accuracy.

Abstract

In almost all situation assessment problems, it is useful to dynamically contract and expand the states under consideration as assessment proceeds. Contraction is most often used to combine similar events or low probability events together in order to reduce computation. Expansion is most often used to make distinctions of interest which have significant probability in order to improve the quality of the assessment. Although other uncertainty calculi, notably Dempster-Shafer [Shafer, 1976], have addressed these operations, there has not yet been any approach of refining and coarsening state spaces for the Bayesian Network technology. This paper presents two operations for refining and coarsening the state space in Bayesian Networks. We also discuss their practical implications for knowledge acquisition.