Stochastic gradient Hamiltonian Monte Carlo (SGHMC) is a variant of stochastic gradients
with momentum where a controlled and properly scaled Gaussian noise is added to the …

In this article, we construct and analyse an explicit numerical splitting method for a class of
semi-linear stochastic differential equations (SDEs) with additive noise, where the drift is …

We discuss parametric estimation of a degenerate diffusion system from time-discrete
observations. The first component of the degenerate diffusion system has a parameter 𝜃 1 in …

We provide a comparative analysis of qualitative features of different numerical methods for
the inhomogeneous geometric Brownian motion (IGBM). The limit distribution of the IGBM …

Abstract The FitzHugh–Nagumo is a well-known neuronal model that describes the
generation of spikes at the intracellular level. We study a stochastic version of the model …

This paper proposes a general higher order operator splitting scheme for diffusion
semigroups using the Baker–Campbell–Hausdorff type commutator expansion of non …

We study parametric inference for ergodic diffusion processes with a degenerate diffusion
matrix. Existing research focuses on a particular class of hypo-elliptic Stochastic Differential …

We consider the quasi-likelihood analysis for a linear regression model driven by a Student-t
L\'evy process with constant scale and arbitrary degrees of freedom. The model is observed …

Suppose X is a multidimensional diffusion process. Assume that at time zero the state of X is
fully observed, but at time only linear combinations of its components are observed. That is …

This work aims at making a comprehensive contribution in the general area of parametric
inference for discretely observed diffusion processes. Established approaches for likelihood …