In this paper we introduce decompositions of diffusion measure which are used to construct an algorithm for the exact simulation of diffusion sample paths and of diffusion hitting times of smooth boundaries. We consider general classes of scalar time-inhomogeneous diffusions and certain classes of multivariate diffusions. The methodology presented in this paper is based on a novel construction of the Brownian bridge with known range for its extrema, which is of interest on its own right
This article develops a class of Monte Carlo (MC) methods for simulating conditioned diffusion samp...
Many biological, chemical and physical problems can be reduced to that of the diffusion of a particl...
Abstract. We propose a method for estimating first passage time densities of one-dimensional diffusi...
We propose simple methods for multivariate diffusion bridge simulation, which plays a fundamental ro...
With a view to likelihood inference for discretely observed diffusion type models, we propose a simp...
We present and study a Langevin MCMC approach for sampling nonlinear diffusion bridges. The method i...
Revised with new numerical examplesWe consider the problem of simulating diffusion bridges, which ar...
Computing expected values of functions involving extreme values of diffusion processes can find wide...
Many approaches for conducting Bayesian inference on discretely observed diffusions involve imputing...
We introduce the use of the Zig-Zag sampler to the problem of sampling conditional diffusion process...
This paper introduces a framework for simulating finite dimensional representations of (jump) diffus...
The problem of simulation of phase trajectories of a diffusion process in a bounded domain is consid...
simulation of diffusion bridges with application to likelihood inference for diffusion
International audienceSince diffusion processes arise in so many different fields, efficient technic...
The recent advent of modern technology has generated a large number of datasets which can be frequen...
This article develops a class of Monte Carlo (MC) methods for simulating conditioned diffusion samp...
Many biological, chemical and physical problems can be reduced to that of the diffusion of a particl...
Abstract. We propose a method for estimating first passage time densities of one-dimensional diffusi...
We propose simple methods for multivariate diffusion bridge simulation, which plays a fundamental ro...
With a view to likelihood inference for discretely observed diffusion type models, we propose a simp...
We present and study a Langevin MCMC approach for sampling nonlinear diffusion bridges. The method i...
Revised with new numerical examplesWe consider the problem of simulating diffusion bridges, which ar...
Computing expected values of functions involving extreme values of diffusion processes can find wide...
Many approaches for conducting Bayesian inference on discretely observed diffusions involve imputing...
We introduce the use of the Zig-Zag sampler to the problem of sampling conditional diffusion process...
This paper introduces a framework for simulating finite dimensional representations of (jump) diffus...
The problem of simulation of phase trajectories of a diffusion process in a bounded domain is consid...
simulation of diffusion bridges with application to likelihood inference for diffusion
International audienceSince diffusion processes arise in so many different fields, efficient technic...
The recent advent of modern technology has generated a large number of datasets which can be frequen...
This article develops a class of Monte Carlo (MC) methods for simulating conditioned diffusion samp...
Many biological, chemical and physical problems can be reduced to that of the diffusion of a particl...
Abstract. We propose a method for estimating first passage time densities of one-dimensional diffusi...