We propose simple methods for multivariate diffusion bridge simulation, which plays a fundamental role in simulation-based likelihood and Bayesian inference for stochastic differential equations. By a novel application of classical coupling methods, the new approach generalizes a previously proposed simulation method for one-dimensional bridges to the multi-variate setting. First a method of si...

The simulation of correlated multivariate Poisson processes with negative correlation between their components has many important applications in Finance, Insurance, Geophysics, and other areas applied probability. Introduced our earlier work, the Backward Simulation (BS) approach to is able capture a wide range correlation, including extreme positive that not possible approaches such as forwar...

We develop and analyze a class of unbiased Monte Carlo estimators for multivariate jump-diffusion processes with state-dependent drift, volatility, jump intensity size. A change measure argument is used to extend existing the inter-arrival diffusion include jumps. Under standard regularity conditions on coefficient target functions, we prove unbiasedness finite variance properties resulting est...

It is now common in the mining industry to deal with several correlated attributes, which need to be jointly simulated in order to reproduce their correlations and assess the multivariate grade risk reasonably. Approaches to multivariate simulation which remove the correlation between attributes of interest prior to simulate and then re-impose the relationship afterward have been gaining popula...