Continuous/discrete non parametric Bayesian belief nets with UNICORN and UNINET

Abstract

Hanea et al. (2006) presented a method for quantifying and computing continuous/discrete non parametric Bayesian Belief Nets (BBN). Influences are represented as conditional rank correlations, and the joint normal copula enables rapid sampling and conditionalization. Further mathematical background is in Kurowicka and Cooke (2007). This article sketches the current stage of development. The driving application currently involves 133 continuous and discrete probabilistic nodes, and 330 functional nodes. Boolean functions enable fault trees to be fully represented as functional nodes in a BBN. Repeated nodes are easily handled with the identity function. Current perspectives and challenges conclude the paper.