Minimum uncertainty as Bayesian network model selection principle.

ABSTRACT:

Background

Bayesian Network (BN) modeling is a prominent methodology in computational systems biology. However, the incommensurability of datasets frequently encountered in life science domains gives rise to contextual dependence and numerical irregularities in the behavior of model selection criteria (such as MDL, Minimum Description Length) used in BN reconstruction. This renders model features, first and foremost dependency strengths, incomparable and difficult to interpret. In this study, we derive and evaluate a model selection principle that addresses these problems.

Results

The objective of the study is attained by (i) approaching model evaluation as a misspecification problem, (ii) estimating the effect that sampling error has on the satisfiability of conditional ind

SUBMITTER: Gogoshin G 

PROVIDER: S-EPMC11980298 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature