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CATEGORIES:Statistics
SUMMARY:A Bayesian nonparametric approach to testing for d
ependence between random variables - Sarah Filipp
i (Oxford)
DTSTART;TZID=Europe/London:20160603T160000
DTEND;TZID=Europe/London:20160603T170000
UID:TALK65900AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/65900
DESCRIPTION:Nonparametric and nonlinear measures of statistica
l dependence between pairs of random variables are
important tools in modern data analysis. In parti
cular the emergence of large data sets can now sup
port the relaxation of linearity assumptions impli
cit in traditional association scores such as corr
elation. Here we describe a Bayesian nonparametric
procedure that leads to a tractable\, explicit an
d analytic quantification of the relative evidence
for dependence vs independence. Our approach uses
Polya tree priors on the space of probability mea
sures which can then be embedded within a decision
theoretic test for dependence. Polya tree priors
can accommodate known uncertainty in the form of
the underlying sampling distribution and provides
an explicit posterior probability measure of both
dependence and independence. Well known advantages
of having an explicit probability measure include
: easy comparison of evidence across different stu
dies\; encoding prior information\; quantifying ch
anges in dependence across different experimental
conditions\, and\; the integration of results with
in formal decision analysis.
LOCATION:MR12\, Centre for Mathematical Sciences\, Wilberfo
rce Road\, Cambridge.
CONTACT:Quentin Berthet
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