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SUMMARY:The Bernstein-von Mises theorem for semiparametric mixture - Stefa
 n  Franssen  (CNRS\, Université Sorbonne Paris Nord)
DTSTART:20250812T093000Z
DTEND:20250812T103000Z
UID:TALK235015@talks.cam.ac.uk
DESCRIPTION:Mixture models are a powerful tool for modelling systems with 
 latent variables. Our goal is to estimate some parameter of interest whose
  distribution depends on some latent variables. Using Bayesian methods\, w
 e can find the posterior distribution and get point estimates and uncertai
 nty quantification. However\, it is a priori unclear how reliable the Baye
 sian inference is. With a Bernstein-von Mises theorem we provide a frequen
 tist guarantee for the reliability and asymptotic optimality of the infere
 nce. We prove a general BvM theorem and apply it to two specific mixture m
 odels: the frailty models and Errors-In-Variables model.
LOCATION:Seminar Room 2\, Newton Institute
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