On Graphical Models for Dynamic Systems

Alberto Lekuona, Beatriz Lacruz, Pilar Lasala
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:317-323, 1995.

Abstract

It is widely recognized that probabilistic graphical models provide a good framework for both knowledge representation and probabilistic inference (e.g., see [2],[14]). The dynamic behaviour of a system which changes over the time needs an implicit or explicit time representation. In this paper, an implicit time representation using dynamic graphical models is proposed. Our goal is to model the state of a system and its evolution over the time in a richer and more natural way than other approaches together with a more suitable treatment of the inference on variables of interest.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-lekuona95a, title = {On Graphical Models for Dynamic Systems}, author = {Lekuona, Alberto and Lacruz, Beatriz and Lasala, Pilar}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {317--323}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/lekuona95a/lekuona95a.pdf}, url = {https://proceedings.mlr.press/r0/lekuona95a.html}, abstract = {It is widely recognized that probabilistic graphical models provide a good framework for both knowledge representation and probabilistic inference (e.g., see [2],[14]). The dynamic behaviour of a system which changes over the time needs an implicit or explicit time representation. In this paper, an implicit time representation using dynamic graphical models is proposed. Our goal is to model the state of a system and its evolution over the time in a richer and more natural way than other approaches together with a more suitable treatment of the inference on variables of interest.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T On Graphical Models for Dynamic Systems %A Alberto Lekuona %A Beatriz Lacruz %A Pilar Lasala %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-lekuona95a %I PMLR %P 317--323 %U https://proceedings.mlr.press/r0/lekuona95a.html %V R0 %X It is widely recognized that probabilistic graphical models provide a good framework for both knowledge representation and probabilistic inference (e.g., see [2],[14]). The dynamic behaviour of a system which changes over the time needs an implicit or explicit time representation. In this paper, an implicit time representation using dynamic graphical models is proposed. Our goal is to model the state of a system and its evolution over the time in a richer and more natural way than other approaches together with a more suitable treatment of the inference on variables of interest. %Z Reissued by PMLR on 01 May 2022.
APA
Lekuona, A., Lacruz, B. & Lasala, P.. (1995). On Graphical Models for Dynamic Systems. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:317-323 Available from https://proceedings.mlr.press/r0/lekuona95a.html. Reissued by PMLR on 01 May 2022.

Related Material