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VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Recent advances in statistical-computational theory for PDE models
DTSTART:20260922T161500
DTSTAMP:20260918T141305Z
UID:2915bcdaad3a370d35afe80da18c8efdf621635851b106f3df4acc7b
CATEGORIES:Conferences - Seminars
DESCRIPTION:Sven Wang (EPFL)\nWe present recent statistical and computatio
 nal guarantees which underpin methodologies for parameter inference in com
 plex statistical models\, for instance arising from differential equations
  (PDEs/SDEs). Firstly\, we will discuss statistical convergence guarantees
  with growing statistical sample size. Secondly\, we address recent progre
 ss in studying the (polynomial) computational complexity of the numerical 
 algorithms required. This includes polynomial-time mixing results for high
 -dimensional Markov Chain Monte Carlo (MCMC) methods as well as recent “
 generalized M-estimators” achieving near-linear runtime with respect to 
 the statistical sample size N.\n 
LOCATION:CM 1 517 https://plan.epfl.ch/?room==CM%201%20517
STATUS:CONFIRMED
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