Gibbs Sampling in Factorized Continuous-Time Markov Processes

Abstract

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.

Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)

References in corpus (5)

• Expectation Propagation for approximate Bayesian inference
• Continuous Time Bayesian Networks
• Expectation Propagation for Continuous Time Bayesian Networks
• Reasoning at the Right Time Granularity
• Continuous Time Markov Networks

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• Structure learning for CTBN's via penalized maximum likelihood methods