BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//talks.cam.ac.uk//v3//EN
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:19700329T010000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:19701025T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Optimal Bayesian experimental design: focused obje
 ctives and observation selection strategies - Yous
 sef Marzouk (Massachusetts Institute of Technology
 )
DTSTART;TZID=Europe/London:20180410T090000
DTEND;TZID=Europe/London:20180410T100000
UID:TALK103567AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/103567
DESCRIPTION:I will discuss two complementary efforts in Bayesi
 an optimal experimental design for inverse problem
 s.   The first focuses on evaluating an experiment
 al design objective: we describe a new computation
 al approach for ``focused&#39\;&#39\; optimal Baye
 sian experimental design with nonlinear models\, w
 ith the goal of maximizing expected information ga
 in in targeted subsets of model parameters. Our ap
 proach considers uncertainty in the full set of mo
 del parameters\, but employs a design objective th
 at can exploit learning trade-offs among different
  parameter subsets. We introduce a layered multipl
 e importance sampling scheme that provides consist
 ent estimates of expected information gain in this
  focused setting\, with significant reductions in 
 estimator bias and variance for a given computatio
 nal effort.  The second effort focuses on optimiza
 tion of information theoretic design objectives---
 in particular\, from the combinatorial perspective
  of observation selection. Given many potential ex
 periments\, one may wish to choose a most informat
 ive subset thereof. Even if the data have in princ
 iple been collected\, practical constraints on sto
 rage\, communication\, and computational costs may
  limit the number of observations that one wishes 
 to employ. We introduce methods for selecting near
 -optimal subsets of the data under cardinality con
 straints. Our methods exploit the structure of lin
 ear inverse problems in the Bayesian setting\, and
  can be efficiently implemented using low-rank app
 roximations and greedy strategies based on modular
  bounds.  This is joint work with Chi Feng and Jay
 anth Jagalur-Mohan.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
END:VEVENT
END:VCALENDAR