The statistical problem of parameter estimation in partially observed hypoelliptic diffusion
processes is naturally occurring in many applications. However, because of the noise …

Estimation of parameters of a diffusion based on discrete time observations poses a difficult
problem due to the lack of a closed form expression for the likelihood. From a Bayesian …

Ionic models with two state variables are routinely used in patient specific electro-physiology
simulations due to the small number of parameters to be constrained and their …

The problem of formal likelihood-based (either classical or Bayesian) inference for discretely
observed multidimensional diffusions is particularly challenging. In principle, this involves …

Parameter estimation in multidimensional diffusion models with only one coordinate
observed is highly relevant in many biological applications, but a statistically difficult …

Multidimensional hypoelliptic diffusions arise naturally in different fields, for example to
model neuronal activity. Estimation in those models is complex because of the degenerate …

Multi-dimensional stochastic differential equations (SDEs) are a powerful tool to describe
dynamics of phenomena that change over time. We focus on the parametric estimation of …

Fluidic oscillators display a unique feature: from a constant input flow, they generate an
output that alternates both temporally and spatially, all without the necessity for any moving …

Neurobiological data such as electroencephalography measurements pose a statistical
challenge due to low spatial resolution and poor signal-to-noise ratio, as well as large …

We consider an interacting system of particles with value in $\mathbb {R}^ d\times\mathbb
{R}^ d $, governed by transport and diffusion on the first component, on that may serve as a …