Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks
This addresses computational efficiency issues for researchers and practitioners working with continuous time dynamical systems, but it is incremental as it builds on existing Gibbs sampling methods.
The paper tackled the problem of inferring unobserved paths in Markov jump processes and continuous time Bayesian networks by introducing a fast auxiliary variable Gibbs sampler, demonstrating significant computational benefits over a state-of-the-art Gibbs sampler.
Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserved paths in these models by introducing a fast auxiliary variable Gibbs sampler. Our approach is based on the idea of uniformization, and sets up a Markov chain over paths by sampling a finite set of virtual jump times and then running a standard hidden Markov model forward filtering-backward sampling algorithm over states at the set of extant and virtual jump times. We demonstrate significant computational benefits over a state-of-the-art Gibbs sampler on a number of continuous time Bayesian networks.