2008-07-09-simma08a.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
We present a generative model for representing and reasoning about the relationships among events in continuous time. We apply the model to the domain of networked and distributed computing environments where we fit the parameters of the model from timestamp observations, and then use hypothesis testing to discover dependencies between the events and changes in behavior for monitoring and diagnosis. After introducing the model, we present an EM algorithm for fitting the parameters and then present the hypothesis testing approach for both dependence discovery and change-point detection. We validate the approach for both tasks using real data from a trace of network events at Microsoft Research Cambridge. Finally, we formalize the relationship between the proposed model and the noisy-or gate for cases when time can be discretized.	CT-NOR: representing and reasoning about events in continuous time	2008	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	simma08a	0	CT-NOR: representing and reasoning about events in continuous time	484	493	484-493	484	false	Simma, Aleksandr and Goldszmidt, Moises and MacCormick, John and Barham, Paul and Black, Richard and Isaacs, Rebecca and Mortier, Richard	given family Aleksandr Simma given family Moises Goldszmidt given family John MacCormick given family Paul Barham given family Richard Black given family Rebecca Isaacs given family Richard Mortier	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/simma08a/simma08a.pdf