Bayesian network modelling through qualitative patterns

Peter J.F. Lucas*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

63 Citations (Scopus)

Abstract

In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions offered by the Bayesian-network formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative analogues to Bayesian networks, and allow modelling interactions in terms of qualitative signs. They thus have the advantage that developers can abstract from the numerical detail, and therefore the gap may not be as wide as for their quantitative counterparts. A notion that has been suggested in the literature to facilitate Bayesian-network development is causal independence. It allows exploiting compact representations of probabilistic interactions among variables in a network. In the paper, we deploy both causal independence and QPNs in developing and analysing a collection of qualitative, causal interaction patterns, called QC patterns. These are endowed with a fixed qualitative semantics, and are intended to offer developers a high-level starting point when developing Bayesian networks.

Original languageEnglish
Pages (from-to)233-263
Number of pages31
JournalArtificial intelligence
Volume163
Issue number2
DOIs
Publication statusPublished - Apr 2005
Externally publishedYes

Keywords

  • Bayesian networks
  • Knowledge representation
  • Qualitative reasoning
  • n/a OA procedure

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