Sequential importance sampling for structural reliability

This paper discusses the application of sequential importance sampling (SIS) to the estimation of the probability of failure in structural reliability. SIS was developed originally in the statistical community for exploring posterior distributions and estimating normalizing constants. The basic idea is to gradually translate samples from the prior distribution to samples from the posterior distribution through a sequential reweighting operation. In the context of structural reliability, SIS can be applied to produce samples of an approximately optimal importance sampling density, which can then be used for estimating the sought probability through importance sampling. The transition of the samples is defined through the construction of a sequence of intermediate distributions. We discuss a particular choice of the intermediate distributions and the properties of the derived algorithm. Moreover, we introduce an MCMC algorithm for application within the SIS procedure that is especially efficient for tackling high-dimensional problems.     «

This paper discusses the application of sequential importance sampling (SIS) to the estimation of the probability of failure in structural reliability. SIS was developed originally in the statistical community for exploring posterior distributions and estimating normalizing constants. The basic idea is to gradually translate samples from the prior distribution to samples from the posterior distribution through a sequential reweighting operation. In the context of structural reliability, SIS...     »