BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Radu Stoica (Université de Lorraine) DTSTART:20240424T111500Z DTEND:20240424T120000Z DTSTAMP:20260422T155200Z UID:gbgstats/51 DESCRIPTION:Title: Approximated inference for marked Gibbs point process\nby Radu Stoic a (Université de Lorraine) as part of Gothenburg statistics seminar\n\nLe cture held in MVL14.\n\nAbstract\nParameter estimation for point processes is achieved via solving optimisation problems built using general strateg ies. Three well established strategies are enumerated. The first consists of considering contrast fuctions based on summary statistics. The second o ne uses the pseudo-likelihood. And the third approximates the likelihood f unction via Monte Carlo procedures. Each of these techniques has known adv antages and drawbacks (Moler and Waagepetersen 2004\, van Lieshout 2001\, 2019).\n\nSampling point process posterior densities is an inference appro ach deeply intertwinned wih the previous one\, since it allows simultaneou s parameter estimation and statistical tests based on observations. The au xiliary variable method (Moller et al.\,2006) gives the mathematical solut ion to this problem\, while pointing out the difficulties of its practical implementation due to poor mixing. The exchange algorithm proposed by (Mu rray et al. 2006)\, (Caimo and Friel\, 2011) proposes a solution for the p oor mixing induced by the auxiliary variable method. As its predecessor it requires exact simulation for the sampling of the auxiliary variable. Thi s is not really a drawback\, but it may explode the computational time for models exhibiting strong interactions (van Lieshout and Stoica\, 2006). \ n\nThis talk presents the approximate ABC Shadow and SSA methods as comple mentary inference methods to the ones based on posterior density sampling. These methods do not require exact simulation\, while providing the neces sary theoretical control. The derived algorithms are applied on data from several application domains such as astronomy\, geosciences and network s ciences (Stoica et al.\,17)\, (Stoica et al.\,21)\, (Hurtado et al.\,21)\, (Laporte et al.\,22).\n LOCATION:https://researchseminars.org/talk/gbgstats/51/ END:VEVENT END:VCALENDAR