DocumentCode :
2615270
Title :
Efficient suboptimal rare-event simulation
Author :
Zhang, Xiaowei ; Blanchet, Jose ; Glynn, Peter W.
Author_Institution :
Stanford Univ., Stanford
fYear :
2007
fDate :
9-12 Dec. 2007
Firstpage :
389
Lastpage :
394
Abstract :
Much of the rare-event simulation literature is concerned with the development of asymptotically optimal algorithms. Because of the difficulties associated with applying these ideas to complex models, this paper focuses on sub-optimal procedures that can be shown to be much more efficient than conventional crude Monte Carlo. We provide two such examples, one based on "repeated acceptance/rejection" as a mean of computing tail probabilities for hitting time random variables and the other based on filtered conditional Monte Carlo.
Keywords :
discrete event simulation; probability; asymptotically optimal algorithms; suboptimal rare-event simulation; tail probabilities; time random variables; Chebyshev approximation; Computational modeling; Discrete event simulation; Engineering management; Monte Carlo methods; Probability; Random variables; Statistics; Tail; Upper bound;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Simulation Conference, 2007 Winter
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-1306-5
Electronic_ISBN :
978-1-4244-1306-5
Type :
conf
DOI :
10.1109/WSC.2007.4419627
Filename :
4419627
Link To Document :
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