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 …

the problem of using simulation to estimate the number of transitions it takes the process to enter \scr{A}. Using …

In a discrete-event simulation, the information related to the events scheduled to occur in the future is kept in a …

approach to simulate comparative solutions for specific contracts under different parametric settings. Through a simulation …

sources of uncertainty as catch per species, catch per unit of effort, weather, and markets by a computer simulation procedure …

We analyze the computational problem of estimating financial risk in a nested simulation. In this approach, an outer … simulation is used to generate financial scenarios, and an inner simulation is used to estimate future portfolio values in each …. Our algorithm sequentially allocates computational effort in the inner simulation based on marginal changes in the risk …