Continuous-Time Belief Propagation

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Continuous-Time Belief Propagation
Many temporal processes can be naturally modeled as a stochastic system that evolves continuously over time. The representation language of continuous-time Bayesian networks allows to succinctly describe multi-component continuous-time stochastic processes. A crucial element in applications of such models is (approximate) inference. Here we introduce a variational approximation scheme, which is a natural extension of Belief Propagation for continuoustime processes. In this scheme, we view messages as inhomogeneous Markov processes over individual components. This leads to a relatively simple procedure that allows to easily incorporate adaptive ordinary differential equation (ODE) solvers to perform individual steps. We provide the theoretical foundations for the approximation, and show how it performs on a range of networks. Our results demonstrate that our method is quite accurate on singly connected networks, and provides close approximations in more complex ones.
Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman
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