Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/86762 
Year of Publication: 
2009
Series/Report no.: 
Tinbergen Institute Discussion Paper No. 09-084/4
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
There are various importance sampling schemes to estimate rare event probabilities in Markovian systems such as Markovian reliability models and Jackson networks. In this work, we present a general state dependent importance sampling method which partitions the state space and applies the cross-entropy method to each partition. We investigate two versions of our algorithm and apply them to several examples of reliability and queueing models. In all these examples we compare our method with other importance sampling schemes. The performance of the importance sampling schemes is measured by the relative error of the estimator and by the effciency of the algorithm. The results from experiments show considerable improvements both in running time of the algorithm and the variance of the estimator.
Subjects: 
Cross-Entropy
Rare Events
Importance Sampling
Large-Scale Markov Chains
JEL: 
C6
Document Type: 
Working Paper

Files in This Item:
File
Size
739.55 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.