Quantitative inference by qualitative semantic knowledge mining with Bayesian model averaging

Rui Chang, Martin Stetter, Wilfried Brauer

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

In this paper, we consider the problem of performing quantitative Bayesian inference and model averaging based on a set of qualitative statements about relationships. Statements are transformed into parameter constraints which are imposed onto a set of Bayesian networks. Recurrent relationship structures are resolved by unfolding in time to Dynamic Bayesian networks. The approach enables probabilistic inference by model averaging, i.e., it allows to predict probabilistic quantities from a set of qualitative constraints without probability assignment on the model parameters. Model averaging is performed by Monte Carlo integration techniques. The method is applied to a problem in a molecular medical context: We show how the rate of breast cancer metastasis formation can be predicted based solely on a set of qualitative biological statements about the involvement of proteins in metastatic processes.

Original languageEnglish (US)
Article number4515867
Pages (from-to)1587-1600
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume20
Issue number12
DOIs
StatePublished - Dec 2008
Externally publishedYes

Keywords

  • Bayesian model averaging
  • Bayesian network inference
  • Bayesian networks
  • Bone metastasis
  • Breast cancer
  • Monte Carlo simulation
  • Qualitative knowledge modeling
  • Qualitative probabilistic network

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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