Structured Proportional Jump Processes

Tal El-Hay, Omer Weissbrod, Elad Eban, Maurizio Zazzi, Francesca Incardona
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:735-744, 2014.

Abstract

Learning the association between observed variables and future trajectories of continuous- time stochastic processes is a fundamental task in dynamic modeling. Often the dynamics are non-homogeneous and involve a large number of interacting components. We introduce a conditional probabilistic model that captures such dynamics, while maintaining scalability and providing an explicit way to express the interrelation between the system components. The principal idea is a factorization of the model into two distinct elements: one depends only on time and the other depends on the system configuration. We developed a learning procedure, given either full or point observations, and tested it on simulated data. We applied the proposed modeling scheme to study large cohorts of diabetes and HIV patients, and demonstrate that the factorization helps shed light on the dynamics of these diseases.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-el-hay14a, title = {Structured Proportional Jump Processes}, author = {El-Hay, Tal and Weissbrod, Omer and Eban, Elad and Zazzi, Maurizio and Incardona, Francesca}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {735--744}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/el-hay14a/el-hay14a.pdf}, url = {https://proceedings.mlr.press/r12/el-hay14a.html}, abstract = {Learning the association between observed variables and future trajectories of continuous- time stochastic processes is a fundamental task in dynamic modeling. Often the dynamics are non-homogeneous and involve a large number of interacting components. We introduce a conditional probabilistic model that captures such dynamics, while maintaining scalability and providing an explicit way to express the interrelation between the system components. The principal idea is a factorization of the model into two distinct elements: one depends only on time and the other depends on the system configuration. We developed a learning procedure, given either full or point observations, and tested it on simulated data. We applied the proposed modeling scheme to study large cohorts of diabetes and HIV patients, and demonstrate that the factorization helps shed light on the dynamics of these diseases.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Structured Proportional Jump Processes %A Tal El-Hay %A Omer Weissbrod %A Elad Eban %A Maurizio Zazzi %A Francesca Incardona %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-el-hay14a %I PMLR %P 735--744 %U https://proceedings.mlr.press/r12/el-hay14a.html %V R12 %X Learning the association between observed variables and future trajectories of continuous- time stochastic processes is a fundamental task in dynamic modeling. Often the dynamics are non-homogeneous and involve a large number of interacting components. We introduce a conditional probabilistic model that captures such dynamics, while maintaining scalability and providing an explicit way to express the interrelation between the system components. The principal idea is a factorization of the model into two distinct elements: one depends only on time and the other depends on the system configuration. We developed a learning procedure, given either full or point observations, and tested it on simulated data. We applied the proposed modeling scheme to study large cohorts of diabetes and HIV patients, and demonstrate that the factorization helps shed light on the dynamics of these diseases. %Z Reissued by PMLR on 04 October 2026.
APA
El-Hay, T., Weissbrod, O., Eban, E., Zazzi, M. & Incardona, F.. (2014). Structured Proportional Jump Processes. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:735-744 Available from https://proceedings.mlr.press/r12/el-hay14a.html. Reissued by PMLR on 04 October 2026.

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