We consider the problem of estimating blocking probabilities in a multicast loss system via simulation, applying the static Monte Carlo method with importance sampling. An approach is introduced where the original estimation problem is first decomposed into independent simpler sub-problems, each roughly corresponding to estimating the blocking probability contribution from a single link. Then w...

Disease surveillance in wildlife populations involves detecting the presence of a disease, characterizing its prevalence and spread, and subsequent monitoring. A probability sample of animals selected from the population and corresponding estimators of disease prevalence and detection provide estimates with quantifiable statistical properties, but this approach is rarely used. Although wildlife...

This article reviews several basic statistical tools needed for modeling data with sampling weights that are implemented in Mplus Version 3. These tools are illustrated in simulation studies for several latent variable models including factor analysis with continuous and categorical indicators, latent class analysis, and growth models. The pseudomaximum likelihood estimation method is reviewed ...

In this paper, we propose a Cross Entropy (CE) [1] based multiagent approach for solving static/dynamic traffic assignment problems (TAP). This algorithm utilizes a family of probability distributions in order to guide travelers (agents) to network equilibrium. The route choice probability distribution depends on the average network performance experienced by agents on previous days. Based on t...

| An adaptive on-line learning method is presented to faciliate pattern classiication using active sampling to identify optimal decision boundary for a stochas-tic oracle with minimum number of training samples. The strategy of sampling at the current estimate of the decision boundary is shown to be optimal compared to random sampling in the sense that the probability of convergence toward the ...

usually, in monitoring schemes the nominal value of the process parameter is assumed known. however, this assumption is violated owing to costly sampling and lack of data particularly in healthcare systems. on the other hand, applying a fixed control limit for the risk-adjusted bernoulli chart causes to a variable in-control average run length performance for patient populations with dissimilar...

A method based on multicanonical Monte Carlo is applied to the calculation of large deviations in the largest eigenvalue of random matrices. The method is successfully tested with the Gaussian orthogonal ensemble, sparse random matrices, and matrices whose components are subject to uniform density. Specifically, the probability that all eigenvalues of a matrix are negative is estimated in these...

in order to investigate effective factors on bread waste and determine effect genial of each factor on probability of being each family in three categories of low-medium, high and extra high bread waste, the order logit model and the 352 families data from mashad city were taken in use. the samples have been selected using a simple random sampling method. the survey of marginal effect in order ...

The reduced density matrix of excitons coupled to a phonon bath at a finite temperature is studied using the path integral Monte Carlo method. Appropriate choices of estimators and importance sampling schemes are crucial to the performance of the Monte Carlo simulation. We show that by choosing the population-normalized estimator for the reduced density matrix, an efficient and physically-meani...