Sampling for Approximate Inference in Continuous Time Bayesian Networks.
Yu Fan, Christian R. Shelton · 2008
We first present a sampling algorithm for continuous time Bayesian networks based on importance sampling. We then extend it to continuous-time particle filtering and smoothing algorithms. The three algorithms can estimate the expectation of any function of a trajectory, conditioned on any evidence set constraining the values of subsets of the variables over subsets of the timeline. We present experimental results on their accuracies and time efficiencies, and compare them to expectation propagation. 1