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DTSTART:20170326T030000
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DTSTART:20171029T020000
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DTSTAMP:20260428T174515Z
UID:58761c772d1de668038487@ist.ac.at
DTSTART:20170419T160000
DTEND:20170419T180000
DESCRIPTION:Speaker: Richard Nickl\nhosted by Laszlo Erdös\nAbstract: In t
 he age of `data science'\, most important inverse problems encountered in 
 applied mathematics\, for instance when one wants to recover parameter coe
 fficients of PDEs/diffusions/jump processes\, are naturally modelled to in
 clude statistical noise or random measurement error. Standard numerical me
 thods to solve inverse problems are generally not robust to the presence o
 f noise\, particularly for non-linear problems. In the last years the Baye
 sian approach has been put forward as a general paradigm to solve statisti
 cal inverse problems in a generic way\, most notably by Andrew Stuart and 
 co-authors\, who have devised successful MCMC algorithms to compute poster
 ior distributions in such settings. A key mathematical question is what re
 covery guarantees can be given for these algorithms and the associated sta
 tistical inference ('uncertainty quantification') procedures. We will disc
 uss some recent theorems that indeed demonstrate that the Bayes formalism 
 successfully\, and optimally\, solves many relevant inverse problems\, bas
 ed on proving frequentist contraction and Laplace-Bernstein-von Mises resu
 lts for relevant Bayesian posterior distributions.\n\nNo prior knowledge o
 f mathematical statistics will be required for this talk\n
LOCATION:Seminar room Big Ground floor / Office Bldg West (I21.EG.101)\, IS
 TA
ORGANIZER:jdeanton@ist.ac.at
SUMMARY:Richard Nickl: On Bayes solutions of some non-linear statistical in
 verse problems
URL:https://talks-calendar.ista.ac.at/events/413
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