Uncertainty in Artificial Intelligence
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Structured Proportional Jump Processes
Tal El-Hay, Omer Weissbrod, Elad Eban, Maurizio Zazzi, Francesca Incardona
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.
Keywords:
Pages: 172-181
PS Link:
PDF Link: /papers/14/p172-el-hay.pdf
BibTex:
@INPROCEEDINGS{El-Hay14,
AUTHOR = "Tal El-Hay and Omer Weissbrod and Elad Eban and Maurizio Zazzi and Francesca Incardona",
TITLE = "Structured Proportional Jump Processes",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "172--181"
}


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