title: Hailfinder. Tools for and experiences with Bayesian normative modeling.
abstract: Bayes Nets (BNs) and Influence Diagrams (IDs), new tools that use graphic user interfaces to facilitate representation of complex inference and decision structures, will be the core elements of new computer technologies that will make the 21st century the Century of Bayes. BNs are a way of representing a set of related uncertainties. They facilitate Bayesian inference by separating structural information from parameters. Hailfinder is a BN that predicts severe summer weather in Eastern Colorado. Its design led to a number of novel ideas about how to build such BNs. Issues addressed included representation of spatial location, categorization of days, system boundaries, pruning, and methods for eliciting and checking on the appropriateness of conditional probabilities. The technology of BNs is improving rapidly. Especially important is the emergence of ways of reusing fragments of BNs. BNs and IDs are not just important design tools; they also represent a major enhancement of the understanding about how important intellectual tasks typically performed by people should and can be performed.
authors.affiliation: University of California, Los Angeles, USA. wedwards@mizar.usc.edu
authors.forename: W
authors.initials: W
authors.lastname: Edwards
mesh_terms: D001499:Bayes Theorem; D008960:Models, Psychological; D011584:Psychology / Q000379:methods*