A census is a complete enumeration of the population: data are collected from every unit in the population. In a survey, a subset of the population, called a sample, is taken. Census are taken at regular but infrequent intervals, e.g. every 5 or 10 years. In between, surveys are used to update results. The selection and estimation procedures of official surveys are almost always based on previo...

Importance sampling and Metropolis-Hastings sampling (of which Gibbs sampling is a special case) are two methods commonly used to sample multi-variate probability distributions (that is, Bayesian networks). Heretofore, the sampling of Bayesian networks has been done on a conventional “classical computer”. In this paper, we propose methods for doing importance sampling and Metropolis-Hastings sa...

The objective is to calculate the probability, PF, that a device will fail when its inputs, x, are randomly distributed with probability density, p (x), e.g., the probability that a device will fracture when subject to varying loads. Here failure is defined as some scalar function, y (x), exceeding a threshold, T . If evaluating y (x) via physical or numerical experiments is sufficiently expens...

Particle filtering is a sequential Monte Carlo method [3] that uses importance sampling to draw samples from probability distributions. In a particle filter the target state is represented by a point mass particle set that is propagated and updated using conditional probability representations of the motion model and measurement model. Methods that improve the sampling efficiency include [3] re...

We investigate the fundamental conditions on the sampling pattern, i.e., locations of the sampled entries, for finite completability of a low-rank tensor given some components of its Tucker rank. In order to find the deterministic necessary and sufficient conditions, we propose an algebraic geometric analysis on the Tucker manifold, which allows us to incorporate multiple rank components in the...

S U M M A R Y We employ importance reweighting to extend Gibbs sampling (GS) to a larger class of unnormalized, multidimensional probability functions and to reduce the dependence of the results on critical temperature T ∗ , which is sometimes poorly known. Instead of sampling at T ∗ , we sample at several sampling temperatures, T S, in an interval centred on an estimate of T ∗ , correcting the...

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...

A new difference estimator has been constructed in two-phase sampling using two auxiliary variables w and x .The first phase sampling unit has been selected with probability proportional to measure of size and second phase sample is selected with equal probability without replacement. The proposed estimator has been found to be more efficient as compared to Raj (1965) in which single auxiliary ...