@inproceedings{hentwe00,
	abstract = {Virtually any discrete-event simulation can be rigorously defined as a Markov chain evolving on a general state space, and under appropriate conditions, the chain has a unique stationary probability distribution. Many steady-state performance measures can be expressed in terms of the stationary probability distribution of the chain.
	
We would like to apply ``coupling from the past'' algorithms to obtain samples from the stationary probability distribution of such chains. Unfortunately, the structural properties of the chains arising from discrete-event simulations preclude the immediate application of current coupling from the past algorithms. We describe why this is the case, and extend a class of coupling from the past algorithms so that they may be applied in this setting.},
	address = {Urbana-Champaign, Illinois},
	author = {S.~G.\ Henderson and R.~L.\ Tweedie},
	booktitle = {Proceedings of the 38th Annual Allerton Conference on Communication, Control, and Computing},
	date-added = {2016-01-10 16:07:54 +0000},
	date-modified = {2016-01-10 16:07:54 +0000},
	editor = {R.~S.\ Sreenivas and D.~L.\ Jones},
	organization = {University of Illinois},
	pages = {1125--1134},
	title = {Perfect simulation for {M}arkov chains arising from discrete-event simulation},
	url_paper = {pubs/HendersonTweedie.pdf},
	year = 2000}

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Unfortunately, the structural properties of the chains arising from discrete-event simulations preclude the immediate application of current coupling from the past algorithms. We describe why this is the case, and extend a class of coupling from the past algorithms so that they may be applied in this setting.","address":"Urbana-Champaign, Illinois","author":[{"firstnames":["S.~G.\\"],"propositions":[],"lastnames":["Henderson"],"suffixes":[]},{"firstnames":["R.~L.\\"],"propositions":[],"lastnames":["Tweedie"],"suffixes":[]}],"booktitle":"Proceedings of the 38th Annual Allerton Conference on Communication, Control, and Computing","date-added":"2016-01-10 16:07:54 +0000","date-modified":"2016-01-10 16:07:54 +0000","editor":[{"firstnames":["R.~S.\\"],"propositions":[],"lastnames":["Sreenivas"],"suffixes":[]},{"firstnames":["D.~L.\\"],"propositions":[],"lastnames":["Jones"],"suffixes":[]}],"organization":"University of Illinois","pages":"1125–1134","title":"Perfect simulation for Markov chains arising from discrete-event simulation","url_paper":"pubs/HendersonTweedie.pdf","year":"2000","bibtex":"@inproceedings{hentwe00,\n\tabstract = {Virtually any discrete-event simulation can be rigorously defined as a Markov chain evolving on a general state space, and under appropriate conditions, the chain has a unique stationary probability distribution. Many steady-state performance measures can be expressed in terms of the stationary probability distribution of the chain.\n\t\nWe would like to apply ``coupling from the past'' algorithms to obtain samples from the stationary probability distribution of such chains. Unfortunately, the structural properties of the chains arising from discrete-event simulations preclude the immediate application of current coupling from the past algorithms. We describe why this is the case, and extend a class of coupling from the past algorithms so that they may be applied in this setting.},\n\taddress = {Urbana-Champaign, Illinois},\n\tauthor = {S.~G.\\ Henderson and R.~L.\\ Tweedie},\n\tbooktitle = {Proceedings of the 38th Annual Allerton Conference on Communication, Control, and Computing},\n\tdate-added = {2016-01-10 16:07:54 +0000},\n\tdate-modified = {2016-01-10 16:07:54 +0000},\n\teditor = {R.~S.\\ Sreenivas and D.~L.\\ Jones},\n\torganization = {University of Illinois},\n\tpages = {1125--1134},\n\ttitle = {Perfect simulation for {M}arkov chains arising from discrete-event simulation},\n\turl_paper = {pubs/HendersonTweedie.pdf},\n\tyear = 2000}\n\n","author_short":["Henderson, S.","Tweedie, R."],"editor_short":["Sreenivas, R.","Jones, D."],"key":"hentwe00","id":"hentwe00","bibbaseid":"henderson-tweedie-perfectsimulationformarkovchainsarisingfromdiscreteeventsimulation-2000","role":"author","urls":{" 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