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CATEGORIES:Machine Learning @ CUED
SUMMARY:Learning of Milky Way Model Parameters Using Matri
x-variate Data in a New Gaussian Process-based Met
hod - Dr Dalia Chakrabarty (University of Warwick)
DTSTART;TZID=Europe/London:20121115T113000
DTEND;TZID=Europe/London:20121115T123000
UID:TALK41422AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/41422
DESCRIPTION:In this talk I will discuss a new Bayesian non-par
ametric method of predicting the value of the mode
l parameter vector that supports real observed dat
a\, where this measured information is in the form
of a matrix. The information is then expressed as
an unknown\, matrix-variate function of the model
parameter vector and this unknown function is mod
elled using a high-dimensional Gaussian Process. T
he model is trained on a training data set that is
generated (via simulations) at a chosen design se
t. In fact\, in our treatment of the information a
s a vector of corresponding dimensions\, this func
tion is modelled as a vector-variate Gaussian Proc
ess leading to the likelihood being matrix-normal
in nature\, with mean and covariance matrices sugg
ested by the structure of the Gaussian Process in
question. In an effort to learn selected process p
arameters (such as the smoothness parameters) from
the data\, in addition to the unknown model param
eter vector value that supports the real data\, we
write their joint posterior probability\, given t
raining as well as observed data. Inference is per
formed using Transformation-based MCMC. An applica
tion of this method is made to learn feature param
eters of the Milky Way\, using measured and simula
ted data of velocity vectors of stars that live in
the vicinity of the Sun. Learning of the Galactic
parameters with the real data is shown to produce
a similar result to a comparator method that requ
ires a much larger data set\, in order to accompli
sh estimation.\n
LOCATION:Engineering Department\, CBL Room BE-438
CONTACT:Zoubin Ghahramani
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