Irregular-Time Bayesian Networks

Michael Ramati, Yuval Shahar
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:483-490, 2010.

Abstract

In many fields observations are performed ir- regularly along time, due to either measure- ment limitations or lack of a constant im- manent rate. While discrete-time Markov models (as Dynamic Bayesian Networks) in- troduce either inefficient computation or an information loss to reasoning about such processes, continuous-time Markov models assume either a discrete state space (as Continuous-Time Bayesian Networks), or a flat continuous state space (as stochastic dif- ferential equations). To address these prob- lems, we present a new modeling class called Irregular-Time Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, al- lowing substantially more compact represen- tations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learn- ing temporal systems, provided that they are fully observed at the same irregularly spaced time-points, and a semiparametric subclass of ITBNs is introduced to allow further adap- tation to the irregular nature of the available data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-ramati10a, title = {Irregular-Time {B}ayesian Networks}, author = {Ramati, Michael and Shahar, Yuval}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {483--490}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/ramati10a/ramati10a.pdf}, url = {https://proceedings.mlr.press/r8/ramati10a.html}, abstract = {In many fields observations are performed ir- regularly along time, due to either measure- ment limitations or lack of a constant im- manent rate. While discrete-time Markov models (as Dynamic Bayesian Networks) in- troduce either inefficient computation or an information loss to reasoning about such processes, continuous-time Markov models assume either a discrete state space (as Continuous-Time Bayesian Networks), or a flat continuous state space (as stochastic dif- ferential equations). To address these prob- lems, we present a new modeling class called Irregular-Time Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, al- lowing substantially more compact represen- tations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learn- ing temporal systems, provided that they are fully observed at the same irregularly spaced time-points, and a semiparametric subclass of ITBNs is introduced to allow further adap- tation to the irregular nature of the available data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Irregular-Time Bayesian Networks %A Michael Ramati %A Yuval Shahar %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-ramati10a %I PMLR %P 483--490 %U https://proceedings.mlr.press/r8/ramati10a.html %V R8 %X In many fields observations are performed ir- regularly along time, due to either measure- ment limitations or lack of a constant im- manent rate. While discrete-time Markov models (as Dynamic Bayesian Networks) in- troduce either inefficient computation or an information loss to reasoning about such processes, continuous-time Markov models assume either a discrete state space (as Continuous-Time Bayesian Networks), or a flat continuous state space (as stochastic dif- ferential equations). To address these prob- lems, we present a new modeling class called Irregular-Time Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, al- lowing substantially more compact represen- tations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learn- ing temporal systems, provided that they are fully observed at the same irregularly spaced time-points, and a semiparametric subclass of ITBNs is introduced to allow further adap- tation to the irregular nature of the available data. %Z Reissued by PMLR on 04 October 2026.
APA
Ramati, M. & Shahar, Y.. (2010). Irregular-Time Bayesian Networks. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:483-490 Available from https://proceedings.mlr.press/r8/ramati10a.html. Reissued by PMLR on 04 October 2026.

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