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DTSTAMP:20260827T134151Z
DESCRIPTION:Title: Computer Model Emulation Using Deep Gaussian Processes.
 \n\nAbstract: Computer models are often used to explore physical systems. 
 Increasingly\, there are cases where the model is fast\, the code is not r
 eadily accessible to scientists\, but a large suite of model evaluations i
 s available. In these cases\, an “emulator” is used to stand in for the co
 mputer model. This work was motivated by a simulator for the chirp mass of
  binary black hole mergers where no output is observed for large portions 
 of the input space and more than 10^6 simulator evaluations are available.
  This poses two problems: (i) the need to address the discontinuity when o
 bserving no chirp mass\; and (ii) performing statistical inference with a 
 large number of simulator evaluations. The traditional approach for emulat
 ion is to use a stationary Gaussian process (GP) because it provides a fou
 ndation for uncertainty quantification for deterministic systems. We explo
 re the impact of the choices when setting up the deep GP on posterior infe
 rence\, apply the proposed approach to the real application and propose a 
 sequential design approach for identifying new simulations.\n\n \n\nColloq
 uium Colloque des sciences mathématiques du Québec\n	Hybride - HEC\, salle 
 Hélène-Desmarais\, Côte-Sainte-Catherine\n\nWeb site : http://crm.umontrea
 l.ca/colloque-sciences-mathematiques-quebec/index.html#csmq\n
DTSTART:20221028T193000Z
DTEND:20221028T203000Z
SUMMARY:Derek Bingham (Simon Fraser University)
URL:https://www.mcgill.ca/mathstat/channels/event/derek-bingham-simon-frase
 r-university-342839
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