A tool for belief updating over time in Bayesian networks

Abstract

We have developed a tool that facilitates dynamically updating beliefs with time. This tool addresses directed probabilistic inference networks that may contain cycles, and takes into account the time delays associated with observations and decisions. Relevance of different observers may decay at different rates in the same application, and the belief in a hypothesis decays towards the associated prior probability. Simple models with few parameters have been implemented, with a user interface that facilitates changes to the structure and parameters of the graphical model, and associated conditional probabilities.