/output function of the simulation model. Kriging also estimates the variances of the predictions of outputs for input combinations not … conditional simulation …

exact simulation algorithm based on the inversion method. This simulation algorithm adopts three advantages. First, the …

simulation to carry out either classical inference through a simulated score method (simulated EM algorithm) or Bayesian analysis …. The central tools we use to deal with the time series dimension of the models are the scan sampler and the simulation …

We deal with discretization schemes for the simulation of the Heston stochastic volatility model. These simulation … valued using closed-form expressions. For the Heston dynamics an exact simulation method was developed by Broadie and Kaya …, aimed to resolve the disadvantages of this method; one of the main bottlenecks in the exact scheme is the simulation of the …

bias into the simulation results. The magnitude of the bias is difficult to quantify. This paper develops a sampling method … unbiased simulation estimators. It is exemplified for a point process whose intensity is a function of a jump-diffusion process …

portfolios of credit-sensitive assets such as loans and corporate bonds. Monte Carlo simulation is an important tool to perform … computations in these models. This paper develops, analyzes and evaluates two simulation algorithms for these models. The …