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CATEGORIES:Signal Processing and Communications Lab Seminars
SUMMARY:Adaptive MCMC and Bayesian time-frequency analysis
- Richard Everitt\, Dept of Statistics\, Universi
ty of Bristol
DTSTART;TZID=Europe/London:20100203T140000
DTEND;TZID=Europe/London:20100203T150000
UID:TALK22033AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/22033
DESCRIPTION:The spectral density is an important quantity in t
ime series analysis. \nNot only is it used in freq
uency domain analyses of signals\, it is also rele
vant to Markov chain Monte Carlo (MCMC) since the
integrated autocorrelation time (the spectral dens
ity evaluated at frequency zero) is related to the
efficiency of MCMC estimators.\n\nIn this talk we
introduce recursions for estimating the spectral
density (and hence also the autocorrelation time)
online. We then show how these recursions may be
used for two different purposes:\n\n1. We present
an adaptive MCMC algorithm\, in which a computer
can use the online estimates of the autocorrelatio
n time in order to automatically tune the MCMC alg
orithm\; 2. We demonstrate how to perform an onli
ne Bayesian estimation of a time-frequency represe
ntation of a signal (using a particle filter) thro
ugh use of online estimates of the Page spectrum o
f the signal.
LOCATION:LR4\, Engineering\, Department of
CONTACT:Rachel Fogg
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