TY - GEN

T1 - Irregular-time Bayesian Networks

AU - Ramati, Michael

AU - Shahar, Yuval

PY - 2010/1/1

Y1 - 2010/1/1

N2 - In many fields observations are performed ir-regularly 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 at 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.

AB - In many fields observations are performed ir-regularly 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 at 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.

UR - http://www.scopus.com/inward/record.url?scp=80053162393&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:80053162393

SN - 9780974903965

T3 - Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

SP - 484

EP - 491

BT - Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

PB - AUAI Press

ER -