Conditional Monte Carlo with Intermediate Estimations for simulation of Markovian systems
Héctor Cancela$^{1}$, Leslie Murray$^{2}$, Gerardo Rubino$^{3}$
$^{1}$Universidad de la República. Montevideo Uruguay,
$^{2}$Universidad Nacional de Rosario. Rosario Argentina,
$^{3}$Bretagne Atlantique, Campus de Beaulieu Rennes. Rennes France
Schedule:Mon 19th@15:15, Room: A

For systems that are suitable to be modelled by continuous Markov chains, dependability analysis is not always straightforward. When such systems are large and complex, it is usually impossible to compute their dependability measures exactly. An alternative solution is to estimate them by simulation, typically by Monte Carlo simulation. But for highly reliable systems standard simulation can not reach satisfactory accuracy levels (measured by the variance of the estimator) within reasonable computing times. Conditional Monte Carlo with Intermediate Estimations (CMIE) is a simulation method proposal aimed at making accurate estimations of dependability measures on highly reliable Markovian systems. The basis of CMIE is introduced, the unbiasedness of the corresponding estimator is proven, and its variance is shown to be lower than the variance of the standard estimator. A variant of the basic scheme, that applies to large and highly reliable multicomponent systems, is introduced. Some experimental results are shown.


	author 		= {Héctor Cancela and Leslie Murray and Gerardo Rubino},
	title 		= {Conditional Monte Carlo with Intermediate Estimations for simulation of Markovian systems},
	booktitle 	= {2015 XLI Latin American Computing Conference (CLEI), Special Edition},
	pages 		= {9--17},
	year 		= {2015},
	editor 		= {Universidad Católica San Pablo},
	address 	= {Arequipa-Peru},
	month 		= {October},
	organization 	= {CLEI},
	publisher 	= {CLEI},
	url 		= {},
	isbn 		= {978-9972-825-91-0},

Generated by Ernesto Cuadros-Vargas , Sociedad Peruana de Computación-Peru, Universidad Católica San Pablo, Arequipa-Perú