BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Andrea Zanoni (Scuola Normale Superiore) DTSTART:20260528T111500Z DTEND:20260528T120000Z DTSTAMP:20260604T125743Z UID:gbgstats/121 DESCRIPTION:Title: Learning interaction kernels in stochastic particle systems\nby And rea Zanoni (Scuola Normale Superiore) as part of Gothenburg statistics sem inar\n\nLecture held in MVL14.\n\nAbstract\nInference in stochastic intera cting particle systems is increasingly important due to applications in so cial sciences\, physics\, and machine learning. In this talk\, we focus on learning the interaction kernel from observations of a single particle. W e adopt a semi-parametric approach\, expressing the kernel as a generalize d Fourier series with orthogonal polynomials tailored to the problem. The Fourier coefficients are estimated via a variation of the method of moment s applied to the invariant measure of the mean-field dynamics\, resulting in a linear system based on moments approximated from the particle traject ory. We analyze the approximation error and asymptotic behavior of the est imator in the limits of infinite observation time\, large particle number\ , and increasing number of Fourier coefficients. Numerical experiments ill ustrate the effectiveness of the approach. This work is joint with Grigori os A. Pavliotis (Imperial College London).\n LOCATION:https://researchseminars.org/talk/gbgstats/121/ END:VEVENT END:VCALENDAR