An efficient surrogate-aided importance sampling framework for reliability analysis

AN ADAPTIVE IMPORTANCE SAMPLING-BASED ALGORITHM USING THE FIRST-ORDER METHOD FOR STRUCTURAL RELIABILITY

Monte Carlo simulation (MCS) is a useful tool for computation of probability of failure in reliability analysis. However, the large number of samples, often required for acceptable accuracy, makes it time-consuming. Importance sampling is a method on the basis of MCS which has been proposed to reduce the computational time of MCS. In this paper, a new adaptive importance sampling-based algorith...

An Efficient Curvelet Framework for Denoising Images

Wiener filter suppresses noise efficiently. However, it makes the out image blurred. Curvelet preserves the edges of natural images perfectly, but, it produces visual distortion artifacts and fuzzy edges to the restored image, especially in homogeneous regions of images. In this paper, a new image denoising framework based on Curvelet transform and wiener filter is proposed, which can stop nois...

Meta-model-based importance sampling for reliability sensitivity analysis

Reliability sensitivity analysis aims at studying the influence of the parameters in the probabilistic model onto the probability of failure of a given system. Such an influence may either be quantified on a given range of values of the parameters of interest using a parametric analysis, or only locally by means of its partial derivatives. This paper is concerned with the latter approach when t...

Efficient Importance Sampling for Binary Contingency Tables

Importance sampling has been reported to produce algorithms with excellent empirical performance in counting problems. However, the theoretical support for its efficiency in these applications has been very limited. In this paper, we propose a methodology that can be used to design efficient importance sampling algorithms for counting and test their efficiency rigorously. We apply our technique...

Efficient Nonparametric Importance Sampling for Bayesian Inference