global warming policies. Because there are typically profound uncertainties in these models, a simulation approach is often …

portfolios of credit-sensitive assets such as loans and corporate bonds. Monte Carlo simulation is an important tool for … performing computations in these models. This paper develops, analyzes, and evaluates two simulation algorithms for intensity … Zeevi, stochastic models and simulation.</i> …

Sequential sampling problems arise in stochastic simulation and many other applications. Sampling is used to infer the …, as is common in the literature. <i>This paper was accepted by Assaf Zeevi, stochastic models and simulation.</i> …

. The emphasis is on the practical problems and potential of applying the method in the simulation of complex systems. The …

We continue our survey of methods for constructing confidence intervals for steady-state means via simulation by … studying sequential procedures which determine the length of the simulation during the course of the run. Our goal is to … provide the simulation practitioner with some guidance as to which published procedures might actually perform well in …

Consider a stochastic system of such complexity that its performance can only be evaluated by using simulation or …

concomitant variables that are defined on each input process sampled during a queueing simulation. In this paper we present an …

We present a general framework for applying simulation to optimize the behavior of discrete event systems. Our approach … decision parameters. We then show how simulation and the likelihood ratio method can be used to evaluate the performance …

motivated by the simulation of a large industrial project called a land seismic survey, in which project performance is based on …