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 …

In this paper we investigate importance sampling techniques for the simulation of Markovian systems with highly … reliable components. The need for simulation arises because the state space of such systems is typically huge, making numerical … computation inefficient. Naive simulation is inefficient due to the rarity of the system failure events. Failure biasing is a …

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> …

simulation experiment. We also show how different definitions of the sample space yield different variants of the LR method, some …