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.

General conditions for bounded relative error in simulations of highly reliable Markovian systems

1996 ◽  
Vol 28 (03) ◽  
pp. 687-727 ◽  
Author(s):  
Marvin K. Nakayama
Keyword(s):  
Importance Sampling ◽  
Relative Error ◽  
Sampling Methods ◽  
Performance Measure ◽  
Necessary Condition ◽  
Probability Measures ◽  
Necessary And Sufficient Condition ◽  
Markovian Systems ◽  
Necessary And Sufficient ◽  
Markovian System

We establish a necessary condition for any importance sampling scheme to give bounded relative error when estimating a performance measure of a highly reliable Markovian system. Also, a class of importance sampling methods is defined for which we prove a necessary and sufficient condition for bounded relative error for the performance measure estimator. This class of probability measures includes all of the currently existing failure biasing methods in the literature. Similar conditions for derivative estimators are established.

General conditions for bounded relative error in simulations of highly reliable Markovian systems

1996 ◽  
Vol 28 (3) ◽  
pp. 687-727 ◽  
Author(s):  
Marvin K. Nakayama
Keyword(s):  
Importance Sampling ◽  
Relative Error ◽  
Sampling Methods ◽  
Performance Measure ◽  
Necessary Condition ◽  
Probability Measures ◽  
Necessary And Sufficient Condition ◽  
Markovian Systems ◽  
Necessary And Sufficient ◽  
Markovian System

We establish a necessary condition for any importance sampling scheme to give bounded relative error when estimating a performance measure of a highly reliable Markovian system. Also, a class of importance sampling methods is defined for which we prove a necessary and sufficient condition for bounded relative error for the performance measure estimator. This class of probability measures includes all of the currently existing failure biasing methods in the literature. Similar conditions for derivative estimators are established.

Computational Techniques for Probabilistic Analysis of Turbomachinery

1992 ◽  
Author(s):  
H. R. Millwater ◽  
A. J. Smalley ◽  
Y.-T. Wu ◽  
T. Y. Torng ◽  
B. F. Evans
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Importance Sampling ◽  
Probabilistic Analysis ◽  
Sampling Methods ◽  
Forced Response ◽  
Cumulative Distribution ◽  
Computational Techniques ◽  
Response Analysis ◽  
Transmitted Force

This paper reports on some advanced computational techniques for probabilistic analysis of turbomachinery. A description of the requirements for probabilistic analysis and several solution methods are summarized. The traditional probabilistic analysis method, Monte Carlo simulation, and two advanced techniques, the Advanced Mean Value (AMV) method and importance sampling, are discussed. The performance of the Monte Carlo, AMV, and importance sampling methods is explored through a forced response analysis of a two degree-of-freedom Jeffcott rotor model. Variations in rotor weight, shaft length, shaft diameter, Young’s modulus, foundation stiffness, bearing clearance, viscosity, and length are considered. The cumulative distribution function of transmitted force is computed using Monte Carlo simulation and AMV at several RPM. Also, importance sampling is used to compute the probability of transmitted force exceeding a specified limit at several RPM. In both cases, the AMV and importance sampling methods are shown to give accurate solutions with far fewer number of simulations than the Monte Carlo method. These methods enable the engineer to perform accurate and efficient probabilistic analysis of realistic complex rotor dynamic models.

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