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PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
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VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20231211T101737Z
DTSTART:20231213T150000Z
DTEND:20231213T160000Z
SUMMARY:SQUIDS Seminar - A State-Space Perspective on Modelling and Infer
ence for Online Skill Rating
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}b2f0-lq0rfg
97-alf3vi
DESCRIPTION:Schedule:\n- 3:05pm: research talk 'Computational Methods for
Bayesian Imaging with Deep Gaussian Process Priors'\n \nAbstract: In im
age reconstruction\, an accurate quantification of uncertainty is of gre
at importance for informed decision making. Here\, the Bayesian approach
to inverse problems can be used: the image is represented through a ran
dom function that incorporates prior information which is then updated t
hrough Bayes' formula. Finding a prior is difficult. Images often exhibi
t non-stationary effects and multiscale behaviour. Thus\, usual Gaussian
process priors are not suitable. Deep Gaussian processes\, on the other
hand\, encode non-stationary behaviour in a natural way through their h
ierarchical structure. \n\nTo apply Bayes' formula\, one commonly employ
s a Markov chain Monte Carlo method that requires sampling from the prio
r. In the case of deep Gaussian processes\, sampling is especially chall
enging in high dimensions: the associated covariance matrices are large\
, dense\, and changing from sample to sample. A popular strategy towards
decreasing computational complexity is to view Gaussian processes as th
e solutions to a fractional stochastic partial differential equation (SP
DE). In this work\, we investigate efficient computational strategies to
solve the fractional SPDEs occurring in deep Gaussian process sampling.
Indeed\, we employ rational approximations to represent the fractional
operators through sparse matrices and reduce computational cost from cub
ic to near-linear. We test our techniques in standard Bayesian image rec
onstruction problems.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:G.114\, Alan Turing Building\, Manchester
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