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EFFICIENT MONTE-CARLO PROBABILITY INTEGRATION

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Probabilistic and Robust Engineering Design Geotechnical Engineering and Analysis Mathematical Approximation and Integration

Abstract

Conventional Monte-Carlo integration for high reliability systems is extremely inefficient. It is possible to use a rather simple technique, known in the literature as importance sampling, to dramatically improve sampling efficiency (or the in the integral for a given amount of sampling). This technique, unlike some other variance reduction techniques, can be applied in a systematic manner to problems with one or more limit state functions, including unions and intersections of limit states. These applications as well as the theory, are outlined in this report. (Author/TRRL)

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2001Estimation of failure probabilities for intersections of non-linear limit states 1992Simulation in Time-Invariant and Time-Variant Reliability Problems 1991Bridge design with reserve and residual reliability constraints 1994Variance reduction by truncated multimodal importance sampling 1994A benchmark study on importance sampling techniques in structural reliability 1987A Sampling Distribution for System Reliability Assessment

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