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MTTF Estimation Using Importance Sampling on Markov Models

Héctor Cancela 1 Gerardo Rubino 2 Bruno Tuffin 2
2 ARMOR - Architectures and network models
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes, Ecole Nationale Supérieure des Télécommunications de Bretagne
Abstract : Very complex systems occur nowadays quite frequently in many technological areas and they are often required to comply with high dependability standards. To study their availability and reliability characteristics, Markovian models are commonly used. Due to the size and complexity of the systems, and due to the rarity of system failures, both analytical solutions and "crude" simulation can be inefficient or even non-relevant. A number of variance reduction Monte Carlo techniques have been proposed to overcome this difficulty; importance sampling methods are among the most efficient. The objective of this paper is to survey existing importance sampling schemes, to propose some improvements and to discuss on their different properties.
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Contributor : Rapport de Recherche Inria <>
Submitted on : Wednesday, May 24, 2006 - 11:33:46 AM
Last modification on : Thursday, February 11, 2021 - 2:48:03 PM
Long-term archiving on: : Sunday, April 4, 2010 - 11:30:55 PM


  • HAL Id : inria-00073000, version 1


Héctor Cancela, Gerardo Rubino, Bruno Tuffin. MTTF Estimation Using Importance Sampling on Markov Models. [Research Report] RR-3672, INRIA. 1999. ⟨inria-00073000⟩



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