Bayesian Network Modelling by Qualitative Patterns
Peter Lucas, F. van Harmelen · 2002
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. 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. However, only very few types of causal independence are in use today, as only the most obvious ones are really understood. We believe that qualitative probabilistic networks (QPNs) may be useful in helping understand causal independence. Originally, QPNs have been put forward as qualitative analogues to Bayesian networks. In this paper, we deploy 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.