Stochastic simulation
A model for reasoning about persistence and causation
Computational Intelligence
Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Sensor validation using dynamic belief networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Optimal decomposition of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
| Hi-index | 0.00 |
A computational scheme for reasoning about dynamic systems using (causal) probabilistic networks is presented. The scheme is based on the framework of Lauritzen and Spiegel-halter (1988), and may be viewed as a generalization of the inference methods of classical time-series analysis in the sense that it allows description of non-linear, multivariate dynamic systems with complex conditional independence structures. Further, the scheme provides a method for efficient backward smoothing and possibilities for efficient, approximate forecasting methods. The scheme has been implemented on top of the HUGIN shell.