{"ID":6138852,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-10T18:29:56.419431225Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06714","arxiv_id":"2607.06714","title":"Expectation-Maximization algorithm to estimate the forcing parameter of a nonlinear McKean-Vlasov diffusion","abstract":"In this article, we address the problem of estimating a forcing parameter in a stochastic differential equation inspired by a model that describes instantaneous turbulent kinetic energy. The stochastic differential equation we analyze is of the nonlinear McKean-Vlasov type, where the drift term depends on a power of the expected value of the solution, which also introduces nonlinearity in an algebraic sense. We propose an estimation algorithm based on the Expectation-Maximization framework and show the consistency of our method. We illustrate our findings through numerical experiments.","short_abstract":"In this article, we address the problem of estimating a forcing parameter in a stochastic differential equation inspired by a model that describes instantaneous turbulent kinetic energy. The stochastic differential equation we analyze is of the nonlinear McKean-Vlasov type, where the drift term depends on a power of th...","url_abs":"https://arxiv.org/abs/2607.06714","url_pdf":"https://arxiv.org/pdf/2607.06714v1","authors":"[\"Eduardo Gutierrez-Turner\",\"Kerlyns Martinez\",\"Hector Olivero\"]","published":"2026-07-07T18:30:54Z","proceeding":"math.ST","tasks":"[\"math.ST\"]","methods":"[\"Diffusion Model\"]","has_code":false}