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

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, discret...

Full description

Saved in:
Bibliographic Details
Published in:International journal of forecasting 1995-03, Vol.11 (1), p.89-111
Main Author: Kjaerulff, Uffe
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian belief network
Discrete Markov chain
Forecasting techniques
Junction tree
Monte Carlo simulation
Monte-Carlo algorithm
Non-linear multivariate dynamic system
Probability
Studies
Time series
Time series analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0169-2070
1872-8200
DOI:10.1016/0169-2070(94)02003-8