Expectation Propagation for Continuous Time Bayesian Networks
arXiv:1207.1401
Abstract
Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. As shown previously, exact inference in CTBNs is intractable. We address the problem of approximate inference, allowing for general queries conditioned on evidence over continuous time intervals and at discrete time points. We show how CTBNs can be parameterized within the exponential family, and use that insight to develop a message passing scheme in cluster graphs and allows us to apply expectation propagation to CTBNs. The clusters in our cluster graph do not contain distributions over the cluster variables at individual time points, but distributions over trajectories of the variables throughout a duration. Thus, unlike discrete time temporal models such as dynamic Bayesian networks, we can adapt the time granularity at which we reason for different variables and in different conditions.
Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)
References in corpus (3)
Cited by in corpus (8)
- Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
- Gibbs Sampling in Factorized Continuous-Time Markov Processes
- Mean Field Variational Approximation for Continuous-Time Bayesian Networks
- Reasoning at the Right Time Granularity
- Continuous Time Markov Networks
- Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks
- CTBNCToolkit: Continuous Time Bayesian Network Classifier Toolkit
- Particle Gibbs algorithms for Markov jump processes