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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20221107T210936Z
DTSTART:20221123T150000Z
DTEND:20221123T160000Z
SUMMARY:Robert Gaunt - Normal approximation for the posterior in exponent
ial families (in-person)
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}nln-la7a0kd
q-7405oq
DESCRIPTION:Robert Gaunt (University of Manchester) will speak in the Pro
bability seminar. (in-person)\n\nThe Bernstein-von Mises (BvM) Theorem i
s a cornerstone in Bayesian statistics. Loosely put\, this theorem recon
ciles Bayesian and frequentist large sample theory by guaranteeing that\
, under regularity conditions\, suitable scalings of posterior distribut
ions are asymptotically normal. In particular\, this implies that the co
ntribution of the prior vanishes in the asymptotic posterior.\n\nIn this
talk\, we demonstrate how the probabilistic technique Stein's method ca
n be used to derive explicit optimal order total variation and Wasserste
in distance bounds to quantify this distributional approximation for pos
terior distributions in exponential family models. We apply our general
bounds to some classical conjugate prior models and observe that the res
ulting bounds have an explicit dependence on the prior distribution and
on sufficient statistics of the data from the sample\, and thus provide
insight into how these factors may affect the quality of the normal appr
oximation.\n\nThis is joint work with Adrian Fischer\, Gesine Reinert an
d Yvik Swan.\n
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Frank Adams Room 2\, Alan Turing Building\, Manchester
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