A nonparametric importance sampling estimator for moment independent importance measures

Generalized moment-independent importance measures based on Minkowski distance

Importance measures have been widely studied and applied in reliability and safety engineering. This paper presents a general formulation of moment-independent importance measures and several commonly discussed importance measures are unified based on Minkowski distance (MD). Moment-independent importance measures can be categorized into three classes of MD importance measures, i.e. probability...

Efficient Nonparametric Importance Sampling for Bayesian Inference

Sequential Importance Sampling for Nonparametric Bayes Models: The Next Generation

There are two generations of Gibbs sampling methods for semiparametric models involving the Dirichlet process. The first generation suffered from a severe drawback: the locations of the clusters, or groups of parameters, could essentially become fixed, moving only rarely. Two strategies that have been proposed to create the second generation of Gibbs samplers are integration and appending a sec...

Nonparametric Adaptive Importance Sampling for Rare Event Simulation

Simulating rare events in telecommunication networks such as estimation for cell loss probability in Asynchronous Transfer Mode (ATM) networks requires a major simulation effort due to the slight chance of buffer overflow. Importance Sampling (IS) is applied to accelerate the occurrence of rare events. Importance Sampling depends on a biasing scheme to make the estimator from IS unbiased. Adapt...

Effective sample size for importance sampling based on discrepancy measures

The Effective Sample Size (ESS) is an important measure of efficiency of Monte Carlo methods such as Markov Chain Monte Carlo (MCMC) and Importance Sampling (IS) techniques. In the IS context, an approximation ÊSS of the theoretical ESS definition is widely applied, involving the inverse of the sum of the squares of the normalized importance weights. This formula, ÊSS, has become an essential p...