2008-07-09-el-hay08a.md

abstract	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
A central task in many applications is reasoning about processes that change over continuous time. Continuous-Time Bayesian Networks is a general compact representation language for multi-component continuous-time processes. However, exact inference in such processes is exponential in the number of components, and thus infeasible for most models of interest. Here we develop a novel Gibbs sampling procedure for multi-component processes. This procedure iteratively samples a trajectory for one of the components given the remaining ones. We show how to perform exact sampling that adapts to the natural time scale of the sampled process. Moreover, we show that this sampling procedure naturally exploits the structure of the network to reduce the computational cost of each step. This procedure is the first that can provide asymptotically unbiased approximation in such processes.	Gibbs sampling in factorized continuous-time Markov processes	2008	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	el-hay08a	0	Gibbs sampling in factorized continuous-time Markov processes	169	178	169-178	169	false	El-Hay, Tal and Friedman, Nir and Kupferman, Raz	given family Tal El-Hay given family Nir Friedman given family Raz Kupferman	2008-07-09	Reissued by PMLR on 09 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	https://raw.githubusercontent.com/mlresearch/r6/main/assets/el-hay08a/el-hay08a.pdf