Irregular-Time Bayesian Networks

Michael Ramati; Yuval Shahar

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arXiv · 1203.3510

Irregular-Time Bayesian Networks

Michael Ramati·Yuval Shahar

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Abstract

In many fields observations are performed irregularly along time, due to either measurement limitations or lack of a constant immanent rate. While discrete-time Markov models (as Dynamic Bayesian Networks) introduce 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 differential equations). To address these problems, we present a new modeling class called Irregular-Time Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, allowing substantially more compact representations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learning 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 adaptation to the irregular nature of the available data.

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Michael Ramati, Yuval Shahar. 2012-03-15. Irregular-Time Bayesian Networks. https://arxiv.org/abs/1203.3510

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