DHugin: a computational system for dynamic time-sliced Bayesian networks

Abstract

A computational system for reasoning about dynamic time-sliced systems using Bayesian networks is presented. The system, called dHugin, may be viewed as a generalization of the inference methods of classical discrete time-series analysis in the sense that it allows description of non-linear, discrete multivariate dynamic systems with complex conditional independence structures. The paper introduces the notions of dynamic time-sliced Bayesian networks, a dynamic time window, and common operations on the time window. Inference, pertaining to the time window and time slices preceding it, are formulated in terms of the well-known message passing scheme in junction trees. Backward smoothing, for example, is performed efficiently through inter-tree message passing. Further, the system provides an efficient Monte-Carlo algorithm for forecasting; i.e. inference pertaining to time slices succeeding the time window. The system has been implemented on top of the Hugin shell.