Many scientific fields have experienced growth in the use of stochastic differential equations
(SDEs), also known as diffusion processes, to model scientific phenomena over time. SDEs …

This paper introduces a framework for simulating finite dimensional representations of
(jump) diffusion sample paths over finite intervals, without discretisation error (exactly), in …

Statistical inference for discretely observed jump-diffusion processes is a complex problem
which motivates new methodological challenges. Thus, existing approaches invariably …

We present a novel inference methodology to perform Bayesian inference for spatiotemporal
Cox processes where the intensity function depends on a multivariate Gaussian process …

Accept-reject-based Markov chain Monte Carlo algorithms have traditionally utilized
acceptance probabilities that can be explicitly written as a function of the ratio of the target …

The paper is focused on problem of filtering random processes in dynamical systems whose
mathematical models are described by stochastic differential equations with a Poisson …

We develop exact Markov chain Monte Carlo methods for discretely sampled, directly and
indirectly observed diffusions. The qualification 'exact'refers to the fact that the invariant and …

We introduce a methodology for online estimation of smoothing expectations for a class of
additive functionals, in the context of a rich family of diffusion processes (that may include …

This paper formulates and analyzes a discretization scheme for a jump-diffusion process
with general state-dependent drift, volatility, jump intensity, and jump size. The jump times of …

Real-world multistage stochastic optimization problems are often characterized by the fact
that the decision maker may take actions only at specific points in time, even if relevant data …