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SUMMARY:Connections between Gaussian Process Regression\, Kalman filtering
  and RTS Smoothing - Simo Särkkä\, Department of Biomedical Engineering 
 and Computational Science Aalto University\, Finland
DTSTART:20110526T130000Z
DTEND:20110526T143000Z
UID:TALK31406@talks.cam.ac.uk
CONTACT:Carl Edward Rasmussen
DESCRIPTION:As already pointed out in the discussion of O'Hagan's 1978 art
 icle\, in\na limited sense\, Gaussian process regression and Kalman filter
 ing can be considered as different formulations of the one and same estima
 tion problem. Strictly speaking this is only true in the case of\none-dime
 nsional input space and we also need a Rauch-Tung-Striebel smoothing step 
 to compute the full posterior. Another point of view is that Gaussian proc
 ess regression can be interpreted as a single update step of an infinite-d
 imensional Kalman filter operating in a Hilbert space\, and thus a natural
  approach to inference in spatio-temporal Gaussian processes is to formula
 te it as an infinite-dimensional state estimation problem. In this talk I 
 will analyze the connections between Gaussian process regression\, Kalman 
 filtering and smoothing\, and discuss the infinite-dimensional Kalman filt
 ering approach to spatio-temporal Gaussian process regression.
LOCATION:Engineering Department\, CBL Room 438
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