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SUMMARY:Gaussian Processes I have Known - Anthony O'Hagan\, University of 
 Sheffield
DTSTART:20210317T110000Z
DTEND:20210317T123000Z
UID:TALK157381@talks.cam.ac.uk
CONTACT:Elre Oldewage
DESCRIPTION:Since I first used Gaussian processes (GPs) in a paper in 1978
 \, they have turned out to be very powerful tools in many application area
 s\, including machine learning.  I will briefly cover several of the areas
  where I have personally used them\, with particular emphasis on two.\n\nT
 he first is the field that has come to be known as uncertainty quantificat
 ion\, which concerns quantifying uncertainty in the predictions of simulat
 ors\, i.e. mechanistic computer codes.  A GP is used to model the simulato
 r\, treating it as a function that maps inputs to outputs\, so this kind o
 f use will be familiar.  The GP is then called an emulator.  But second GP
  models another function\, the model discrepancy\, defined as the differen
 ce between the simulator output and reality - a necessary component becaus
 e no simulation model is perfect.\n\nAnd a related use arises because we c
 an't quantify uncertainty about the outputs unless we quantify uncertainty
  in the inputs\, including the often many uncertain parameters of the simu
 lator.  That involves eliciting the knowledge of experts about those param
 eters and expressing it in the form of a probability distribution.  My sec
 ond application of GPs is to represent uncertainty about the elicited dist
 ribution.
LOCATION:https://eng-cam.zoom.us/j/86068703738?pwd=YnFleXFQOE1qR1h6Vmtwbno
 0LzFHdz09
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