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SUMMARY:Adaptive MCMC and Bayesian time-frequency analysis - Richard Everi
 tt\, Dept of Statistics\, University of Bristol
DTSTART:20100203T140000Z
DTEND:20100203T150000Z
UID:TALK22033@talks.cam.ac.uk
CONTACT:Rachel Fogg
DESCRIPTION:The spectral density is an important quantity in time series a
 nalysis. \nNot only is it used in frequency domain analyses of signals\, i
 t is also relevant to Markov chain Monte Carlo (MCMC) since the integrated
  autocorrelation time (the spectral density evaluated at frequency zero) i
 s related to the efficiency of MCMC estimators.\n\nIn this talk we introdu
 ce recursions for estimating the spectral density (and hence also the auto
 correlation time) online.  We then show how these recursions may be used f
 or two different purposes:\n\n1.  We present an adaptive MCMC algorithm\, 
 in which a computer can use the online estimates of the autocorrelation ti
 me in order to automatically tune the MCMC algorithm\; 2.  We demonstrate 
 how to perform an online Bayesian estimation of a time-frequency represent
 ation of a signal (using a particle filter) through use of online estimate
 s of the Page spectrum of the signal.
LOCATION:LR4\, Engineering\, Department of
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