TY - GEN

T1 - Gibbs sampling in factorized continuous-time Markov processes

AU - El-Hay, Tal

AU - Friedman, Nir

AU - Kupferman, Raz

PY - 2008

Y1 - 2008

N2 - A central task in many applications is reasoning about processes that change over continuous time. Continuous-Time Bayesian Networks is a general compact representation language for multi-component continuous-time processes. However, exact inference in such processes is exponential in the number of components, and thus infeasible for most models of interest. Here we develop a novel Gibbs sampling procedure for multi-component processes. This procedure iteratively samples a trajectory for one of the components given the remaining ones. We show how to perform exact sampling that adapts to the natural time scale of the sampled process. Moreover, we show that this sampling procedure naturally exploits the structure of the network to reduce the computational cost of each step. This procedure is the first that can provide asymptotically unbiased approximation in such processes.

AB - A central task in many applications is reasoning about processes that change over continuous time. Continuous-Time Bayesian Networks is a general compact representation language for multi-component continuous-time processes. However, exact inference in such processes is exponential in the number of components, and thus infeasible for most models of interest. Here we develop a novel Gibbs sampling procedure for multi-component processes. This procedure iteratively samples a trajectory for one of the components given the remaining ones. We show how to perform exact sampling that adapts to the natural time scale of the sampled process. Moreover, we show that this sampling procedure naturally exploits the structure of the network to reduce the computational cost of each step. This procedure is the first that can provide asymptotically unbiased approximation in such processes.

UR - http://www.scopus.com/inward/record.url?scp=80053259149&partnerID=8YFLogxK

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AN - SCOPUS:80053259149

SN - 0974903949

SN - 9780974903941

T3 - Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

SP - 169

EP - 178

BT - Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

T2 - 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

Y2 - 9 July 2008 through 12 July 2008

ER -