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A Guillin

Publications and source records attributed to A Guillin.

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On some topological and spectral properties of kinetic Langevin processes driven by L{\'e}vy noises

We investigate several fundamental properties of kinetic Langevin processes in $\mathbb{R}^{2d}$, defined as solutions to the following system: $$dx\_t = v\_t \, dt, \qquad dv\_t = \mathbf{B}(x\_t, v\_t) \, dt + dL\_t$$ where $(L\_t, t \ge 0)$ is a pure-jump L{\'e}vy process. Our analysis covers both the original process and its killed counterpart, where killing occurs upon exiting domains of the form $\mathscr{D} = \mathscr{O} \times \mathbb{R}^d$ for an arbitrary open set $\mathscr{O} \subset \mathbb{R}^d$. Operating within a low-regularity framework - where the drift $\mathbf{B}$ is not assumed to be continuous - we establish key structural and spectral properties for both the associated non-killed and killed semigroups. These include: the strong Feller property, weak continuity of trajectories with respect to initial conditions, topological irreducibility and the existence of a spectral gap. Furthermore, we prove, in this low-regularity framework, the existence and uniqueness of a weak solution when the driving noise is a rotationally invariant $\alpha$-stable process, when $\alpha \in (1,2)$. For this specific case, we show that the aforementioned properties hold and further establish the existence of densities within certain $L^m$ spaces as well as the Feller $C\_0(\mathbb R^{2d})$-semigroup property. Finally, we address the existence and uniqueness of stationary and quasi-stationary distributions, proving exponential ergodicity for the non-killed process and exponential convergence to the quasi-stationary limit for the conditioned process. We show that these results extend to every $\alpha \in (0,1]$ when the drift is smooth.

math-ph

When Can The Discrete Moran Process May Bereplaced By Wright-fisher Diffusion?

The Moran discrete process and the Wright-Fisher modelare the most popular models in population genetics. It is common tounderstand the dynamics of these models to use an approximating diffusionprocess, called Wright-Fisher diffusion. Here, we give a quantitativelarge population limit of the error committed by using the approximationdiffusion in the presence of weak selection and weak immigrationin one dimension. The approach is robust enough to consider the casewhere selection and immigration are Markovian processes, with limitsjump or diffusion processes.

math.PR