On the Efficient Simulation of the Left-Tail of the Sum of Correlated Log-normal Variates

The sum of Log-normal variates is encountered in many challenging applications such as in performance analysis of wireless communication systems and in financial engineering. Several approximation methods have been developed in the literature, the accuracy of which is not ensured in the tail regions. These regions are of primordial interest wherein small probability values have to be evaluated with high precision. Variance reduction techniques are known to yield accurate, yet efficient, estimates of small probability values. Most of the existing approaches, however, have considered the problem of estimating the right-tail of the sum of Log-normal random variables (RVS). In the present work, we consider instead the estimation of the left-tail of the sum of correlated Log-normal variates with Gaussian copula under a mild assumption on the covariance matrix. We propose an estimator combining an existing mean-shifting importance sampling approach with a control variate technique. The main result is that the proposed estimator has an asymptotically vanishing relative error which represents a major finding in the context of the left-tail simulation of the sum of Log-normal RVs. Finally, we assess by various simulation results the performances of the proposed estimator compared to existing estimators

Прискорене моделювання стаціонарного розподілу кількості вимог у системі SMBAP|G| ∞

Розглядається система масового обслуговування з нескінченною кількістю обслуговуючих пристроїв. В систему надходить груповий потік вимог, який керується напівмарковським процесом. Запропоновано метод прискореного моделювання стаціонарної ймовірності кількості вимог у системі, що ґрунтується на методі істотної вибірки та використовує центральну граничну теорему. Оцінки є асимптотично незміщеними. Виграш в дисперсії порівняно з методом Монте-Карло становить в середньому два порядки.Рассматривается система массового обслуживания с бесконечным количеством обслуживающих устройств. В систему поступает групповой поток требований, управляемый полумарковским процессом. Предложен метод ускоренного моделирования стационарной вероятности количества требований в системе, основанный на методе существенной выборки и использующий центральную предельную теорему. Оценки — асимптотически несмещенные. Выигрыш в дисперсии по сравнению с методом Монте-Карло составляет в среднем два порядка.A queueing system with the infinite number of servers and batch arrival process controlled by the semi-Markov process is investigated. A fast simulation method for the evaluation of the steady-state distribution of the number of customers in the system is proposed, which is based on essential sampling and the central limit theorem. The estimates are asymptotically unbiased. The gain in variance compared to the Monte Carlo method is on the average two orders of magnitude

Прискорене моделювання стаціонарного розподілу кількості вимог у системі SMBAP|G| ∞

Розглядається система масового обслуговування з нескінченною кількістю обслуговуючих пристроїв. В систему надходить груповий потік вимог, який керується напівмарковським процесом. Запропоновано метод прискореного моделювання стаціонарної ймовірності кількості вимог у системі, що ґрунтується на методі істотної вибірки та використовує центральну граничну теорему. Оцінки є асимптотично незміщеними. Виграш в дисперсії порівняно з методом Монте-Карло становить в середньому два порядки.Рассматривается система массового обслуживания с бесконечным количеством обслуживающих устройств. В систему поступает групповой поток требований, управляемый полумарковским процессом. Предложен метод ускоренного моделирования стационарной вероятности количества требований в системе, основанный на методе существенной выборки и использующий центральную предельную теорему. Оценки — асимптотически несмещенные. Выигрыш в дисперсии по сравнению с методом Монте-Карло составляет в среднем два порядка.A queueing system with the infinite number of servers and batch arrival process controlled by the semi-Markov process is investigated. A fast simulation method for the evaluation of the steady-state distribution of the number of customers in the system is proposed, which is based on essential sampling and the central limit theorem. The estimates are asymptotically unbiased. The gain in variance compared to the Monte Carlo method is on the average two orders of magnitude

Dynamic importance sampling for uniformly recurrent markov chains

Importance sampling is a variance reduction technique for efficient estimation of rare-event probabilities by Monte Carlo. In standard importance sampling schemes, the system is simulated using an a priori fixed change of measure suggested by a large deviation lower bound analysis. Recent work, however, has suggested that such schemes do not work well in many situations. In this paper we consider dynamic importance sampling in the setting of uniformly recurrent Markov chains. By ``dynamic'' we mean that in the course of a single simulation, the change of measure can depend on the outcome of the simulation up till that time. Based on a control-theoretic approach to large deviations, the existence of asymptotically optimal dynamic schemes is demonstrated in great generality. The implementation of the dynamic schemes is carried out with the help of a limiting Bellman equation. Numerical examples are presented to contrast the dynamic and standard schemes.Comment: Published at http://dx.doi.org/10.1214/105051604000001016 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Fast simulation of packet loss rates in a shared buffer communications switch

This paper describes an efficient technique for estimating, via simulation, the probability of buffer overflows in a queueing model that arises in the analysis of ATM (Asynchronous Transfer Mode) communication switches. There are multiple streams of (autocorrelated) traffic feeding the switch that has a buffer of finite capacity. Each stream is designated as either being of high or low priority. When the queue length reaches a certain threshold, only high priority packets are admitted to the switch's buffer. The problem is to estimate the loss rate of high priority packets. An asymptotically optimal importance sampling approach is developed for this rare event simulation problem. In this approach, the importance sampling is done in two distinct phases. In the first phase, an importance sampling change of measure is used to bring the queue length up to the threshold at which low priority packets get rejected. In the second phase, a different importance sampling change of measure is used to move the queue length from the threshold to the buffer capacity

On asymptotically efficient simulation of large deviation probabilities

Consider a family of probabilities for which the decay is governed by a large deviation principle. To find an estimate for a fixed member of this family, one is often forced to use simulation techniques. Direct Monte Carlo simulation, however, is often impractical, particularly if the probability that should be estimated is extremely small. Importance sampling is a technique in which samples are drawn from an alternative distribution, and an unbiased estimate is found after a likelihood ratio correction. Specific exponentially twisted distributions were shown to be good sampling distributions under fairly general circumstances. In this paper, we present necessary and sufficient conditions for asymptotic efficiency of a single exponentially twisted distribution, sharpening previously established conditions. Using the insights that these conditions provide, we construct an example for which we explicitly compute the `best' change of measure. However, simulation using the new measure faces exactly the same difficulties as direct Monte Carlo simulation. We discuss the relation between this example and other counterexamples in the liturature. We also apply the conditions to find necessary and sufficient conditions for asymptotic efficiency of the exponential twist in a Mogul'skii sample-path problem. An important special case of this problem is the probability of ruin within finite time