Approximation of distribution laws in population genetics governed by fokker-planck equations

Abstract EN

Stochastic models in population genetics are considered.

Dawson [2] introduced the Gauss-Galerkin method for approximating the density functions of such processes.

We prove convergence for the case where a density function may not exist and we propose a more efficient algorithm for the implementation of the Gauss-Galerkin approximation.

We also provide an example where the method does not allow steady state analysis and we discuss some related open problems.