Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks

Vinayak Rao, Yee Whye Teh
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:685-692, 2011.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR9-rao11a, title = {Fast {MCMC} sampling for {M}arkov jump processes and continuous time {B}ayesian networks}, author = {Rao, Vinayak and Teh, Yee Whye}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {685--692}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/rao11a/rao11a.pdf}, url = {https://proceedings.mlr.press/r9/rao11a.html}, abstract = {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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks %A Vinayak Rao %A Yee Whye Teh %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-rao11a %I PMLR %P 685--692 %U https://proceedings.mlr.press/r9/rao11a.html %V R9 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Rao, V. & Teh, Y.W.. (2011). Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:685-692 Available from https://proceedings.mlr.press/r9/rao11a.html. Reissued by PMLR on 04 October 2026.

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