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SUMMARY:BSU Seminar: 'Methods for Bayesian optimal design of experiments\,
  with application in biological science’ - Prof Dave Woods\, University 
 of Southampton
DTSTART:20230307T140000Z
DTEND:20230307T150000Z
UID:TALK195802@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Bayesian optimal design for many realistic and practically imp
 ortant experiments is complicated by the need to evaluate an expected loss
  over all a priori unknown and unobserved quantities for any prospective d
 esign. For nonlinear statistical models\, this expected loss is typically 
 analytically intractable\; for many physical models\, evaluation of the ex
 pected loss is further complicated by the need for numerical solutions to 
 the underpinning ordinary differential equations (ODEs). Hence\, finding a
 n optimal design for such models via minimisation of the expected loss is 
 challenging. We present methodology for finding optimal designs that (i) t
 reats a Monte Carlo approximation of the expected loss as an unknown black
 -box function and conducts a computer experiment and (ii) employs a probab
 ilistic solution to the system of ODEs\, where necessary.  The potentially
  high-dimensional nature of the problem is mitigated via application of th
 e coordinate exchange algorithm and conditional emulation of the expected 
 loss for each coordinate (value of a single controllable variable in a sin
 gle experimental run). The work is motivated by\, and demonstrated on\, ex
 amples from the biological sciences. 
LOCATION:Large Seminar Room\, East Forvie Building\, Forvie Site\, Robinso
 n Way\, Cambridge CB2 0SR
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