AIFeb 27, 2013
A Bayesian Method Reexamined
arXiv:1302.6781v12 citations
Originality Synthesis-oriented
AI Analysis
This addresses a methodological issue in Bayesian network learning, but it is incremental as it builds on existing work.
The paper identifies counterintuitive results in the K2 network scoring metric for Bayesian networks and proposes a noninformative prior to ensure equivalent networks receive equal scores.
This paper examines the "K2" network scoring metric of Cooper and Herskovits. It shows counterintuitive results from applying this metric to simple networks. One family of noninformative priors is suggested for assigning equal scores to equivalent networks.