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SUMMARY:Distributed Inference (joint work with K. Bleakley and B. Cadre) -
Prof Gerard Biau (Université Pierre et Marie Curie)
DTSTART;VALUE=DATE-TIME:20151022T141500
DTEND;VALUE=DATE-TIME:20151022T151500
UID:https://talks.ox.ac.uk/talks/id/984bc954-5b0d-4832-b7b3-5df9022dfb2d/
DESCRIPTION:The statistical analysis of massive and complex data sets will
require the development of algorithms that depend on distributed computin
g and collaborative inference. Inspired by this\, we propose a collaborati
ve framework that aims to estimate the unknown mean $\\theta$ of a random
variable $X$. In the model we present\, a certain number of calculation un
its\, distributed across a communication network represented by a graph\,
participate in the estimation of $\\theta$ by sequentially receiving indep
endent data from $X$ while exchanging messages via a stochastic matrix $A$
defined over the graph.\n\nWe give precise conditions on the matrix $A$ u
nder which the statistical precision of the individual units is comparable
to that of a (gold standard) virtual centralized estimate\, even though e
ach unit does not have access to all of the data. We show in particular th
e fundamental role played by both the non-trivial eigenvalues of $A$ and t
he Ramanujan class of expander graphs\, which provide remarkable performan
ce for moderate algorithmic cost. \n\n\n\nSpeakers:\nProf Gerard Biau (Uni
versité Pierre et Marie Curie)
LOCATION:1 South Parks Road (Lecture Theatre\, Department of Statistics)\,
1 South Parks Road OX1 3TG
URL:https://talks.ox.ac.uk/talks/id/984bc954-5b0d-4832-b7b3-5df9022dfb2d/
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DESCRIPTION:Talk:Distributed Inference (joint work with K. Bleakley and B.
Cadre) - Prof Gerard Biau (Université Pierre et Marie Curie)
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