Rare event simulation in the context of queueing networks has been an active area of research for more than two decades. A commonly used technique to increase the efficiency of Monte Carlo simulation is importance sampling. However, there are few rigorous results on the design of efficient or asymptotically optimal importance sampling schemes for queueing networks. Using a recently developed ga...

Binomial lattice models nd widespread uses in the valuation of derivative nancial instruments. When these instruments are path-dependent (non-Markovian), it is usually necessary to resort to Monte Carlo simulation. However, a crude Monte Carlo sampling of the lattice can be very ineecient since a large number of sample paths may need to be calculated. We develop procedures for sampling the path...

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...

We investigate in this paper an alternative method to simulation based recursive importance sampling procedure to estimate the optimal change of measure for Monte Carlo simulations. We propose an algorithm which combines (vector and functional) optimal quantization with Newton-Raphson zero search procedure. Our approach can be seen as a robust and automatic deterministic counterpart of recursiv...

The Monte Carlo method gives some estimators to evaluate the expectation E f h] based on samples from either the true density f or from some instrumental density. In this paper, we show that the Riemann sums can be coupled with the importance function. This approach produces a class of Monte Carlo estimators such that the variance is of order O(n ?2). The choice of an optimal estimator among th...

Recently, there has been growing interest in systematic search-based and importance sampling-based lifted inference algorithms for statistical relational models (SRMs). These lifted algorithms achieve significant complexity reductions over their propositional counterparts by using lifting rules that leverage symmetries in the relational representation. One drawback of these algorithms is that t...

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FALL 1999 This article develops a variance-reduction technique for pricing derivatives by simulation in highdimensional multifactor models. A premise of this work is that the greatest gains in simulation efficiency come from taking advantage of the structure of both the cash flows of a security and the model in which it is priced. For this to be feasible in practice requires automating the iden...

In this paper we propose a fast adaptive Importance Sampling method for the efficient simulation of buffer overffow probabilities in queueing networks. The method comprises three stages. First we estimate the minimum Cross-Entropy tilting parameter for a small buffer level; next, we use this as a starting value for the estimation of the optimal tilting parameter for the actual (large) buffer le...