BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Mathis Rost (Chalmers) DTSTART:20251022T111500Z DTEND:20251022T120000Z DTSTAMP:20260422T155052Z UID:gbgstats/99 DESCRIPTION:Title: Void Probabilities and Likelihood Approximation for Gibbs Processes\ nby Mathis Rost (Chalmers) as part of Gothenburg statistics seminar\n\nLec ture held in MVL14.\n\nAbstract\nWhen fitting a model to data\, one would ideally like to use maximum likelihood estimation\, due to its nice statis tical properties. Unfortunately\, the likelihood function\nof a general Gi bbs point process is typically not tractable\, due to the associated norma lizing constant. This has led to the development of a range of alternative methods\,\nsuch as Takacs-Fiksel estimation (including its special case p seudolikelihood estimation) and Point Process Learning.\nLeveraging recent probabilistic results for Gibbs processes\, in this talk we present an\na pproach to perform approximate likelihood estimation for Gibbs processes. Specifically\, we show that the likelihood function can be expressed compl etely in terms of\nthe Papangelou conditional intensity\, which is typical ly known and tractable. This\nnew likelihood representation involves an in finite series expansion\, and we discuss\ndifferent ways of approximating it\, and thereby the likelihood function. We further\ndiscuss how this pla ys out in certain models and compare it to the state-of-the-art.\n LOCATION:https://researchseminars.org/talk/gbgstats/99/ END:VEVENT END:VCALENDAR