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SUMMARY:Bayesian estimation with MCMC
DTSTART:20260526T120000Z
DTEND:20260526T130000Z
DTSTAMP:20260523T230700Z
UID:indico-event-16607@indico.math.cnrs.fr
DESCRIPTION:Speakers: Maxime Egéa (LPSM)\n\nMy talk is motivated by the p
 roblem of sampling from a posterior distribution in Bayesian estimation\, 
 particularly when the posterior concentrates as the number of observations
  increases. In this context\, I will present the Laplace approximation—a
  classical method for approximating concentrated posterior distributions. 
 We will examine its application in the setting of log-concave measures\, a
 nd then extend the discussion to more challenging cases involving multimod
 al distributions. The second part of the talk will be devoted to MCMC algo
 rithm\, and how one can use the previously established results to improve 
 its performance.\n\nhttps://indico.math.cnrs.fr/event/16607/
LOCATION:GI-UTC
URL:https://indico.math.cnrs.fr/event/16607/
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