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 …

In financial and actuarial modeling and other areas of application, stochastic differential
equations with jumps have been employed to describe the dynamics of various state …

We consider analysis of complex stochastic models based upon partial information. MCMC
and reversible jump MCMC are often the methods of choice for such problems, but in some …

The objective of the paper is to present a novel methodology for likelihood-based inference
for discretely observed diffusions. We propose Monte Carlo methods, which build on recent …

We introduce a novel particle filter scheme for a class of partially observed multivariate
diffusions. We consider a variety of observation schemes, including diffusion observed with …

This paper proposes a widely applicable method of approximate maximum-likelihood
estimation for multivariate diffusion process from discretely sampled data. A closed-form …

The chapters of this volume represent the revised versions of the main papers given at the
seventh Séminaire Européen de Statistique on “Statistics for Stochastic Differential …

The demand for automated decision making, learning and inference in uncertain, risk
sensitive and dynamically changing situations presents a challenge: to design …

Diffusion processes are a family of continuous-time continuous-state stochastic processes
that are in general only partially observed. The joint estimation of the forcing parameters and …

This paper is concerned with parameter estimation in linear and non-linear Itô type
stochastic differential equations using Markov chain Monte Carlo (MCMC) methods. The …