scholarly journals Multihistogram reweighting for nonequilibrium Markov processes using sequential importance sampling methods

2013 ◽  
Vol 87 (4) ◽  
Author(s):  
Troels Arnfred Bojesen
Keyword(s):  
Importance Sampling ◽  
Markov Processes ◽  
Sampling Methods ◽  
Sequential Importance Sampling

New importance sampling methods for simulating sequential decoders

1993 ◽  
Vol 39 (5) ◽  
pp. 1716-1722 ◽  
Author(s):  
K.B. Letaief ◽  
J.S. Sadowsky
Keyword(s):  
Importance Sampling ◽  
Sampling Methods

Structural Reliability Analysis With Cross Entropy and Low Discrepancy Sampling Methods

2011 ◽  
Author(s):  
K. Pugazhendhi ◽  
A. K. Dhingra
Keyword(s):  
Monte Carlo ◽  
Importance Sampling ◽  
Structural Reliability ◽  
Sampling Methods ◽  
Reliability Evaluation ◽  
Cross Entropy ◽  
Sampling Density ◽  
Quasi Monte Carlo ◽  
Importance Sampling Techniques ◽  
Optimum Sampling

In recent years quasi Monte-Carlo (QMC) techniques are gaining more popularity for reliability evaluation because of their increased accuracy over traditional Monte-Carlo simulation. A QMC technique like Low Discrepancy Sequence (LDS) combined with importance sampling is shown to be more accurate and robust in the past for the evaluation of structural reliability. However, one of the challenges in using importance sampling techniques to evaluate the structural reliability is to identify the optimum sampling density. In this article, a novel technique based on a combination of cross entropy and low discrepancy sampling methods is used for the evaluation of structural reliability. The proposed technique does not require an apriori knowledge of Most Probable Point of failure (MPP), and succeeds in adaptively identifying the optimum sampling density for the structural reliability evaluation. Several benchmark examples verify that the proposed method is as accurate as the quasi Monte-Carlo technique using low discrepancy sequence with the added advantage of being able to accomplish this without a knowledge of the MPP.

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