A Computational Scheme for Reasoning in Dynamic Probabilistic Networks
Abstract
A computational scheme for reasoning about dynamic systems using (causal) probabilistic networks is presented. The scheme is based on the framework of Lauritzen and Spiegelhalter (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.
Cite
Text
Kjærulff. "A Computational Scheme for Reasoning in Dynamic Probabilistic Networks." Conference on Uncertainty in Artificial Intelligence, 1992. doi:10.1016/B978-1-4832-8287-9.50021-9Markdown
[Kjærulff. "A Computational Scheme for Reasoning in Dynamic Probabilistic Networks." Conference on Uncertainty in Artificial Intelligence, 1992.](https://mlanthology.org/uai/1992/kjrulff1992uai-computational/) doi:10.1016/B978-1-4832-8287-9.50021-9BibTeX
@inproceedings{kjrulff1992uai-computational,
title = {{A Computational Scheme for Reasoning in Dynamic Probabilistic Networks}},
author = {Kjærulff, Uffe},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1992},
pages = {121-129},
doi = {10.1016/B978-1-4832-8287-9.50021-9},
url = {https://mlanthology.org/uai/1992/kjrulff1992uai-computational/}
}