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SUMMARY:Bayesian Adaptive Design for State-space Models with Covariates - 
 Sahu\, S (Southampton)
DTSTART:20110901T100000Z
DTEND:20110901T103000Z
UID:TALK32607@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Modelling data that change over space and time is important in
  many areas\, such as environmental monitoring of air and noise pollution 
 using a sensor network over a long period of time. Often such data are col
 lected dynamically together with the values of a variety of related variab
 les. Due to resource limitations\, an optimal choice (or design) for the l
 ocations of the sensors is important for achieving accurate predictions. T
 his choice depends on the adopted model\, that is\, the spatial and tempor
 al processes\, and the dependence of the responses on relevant covariates.
  We investigate adaptive designs for state-space models where the selectio
 n of locations at time point $t_{n+1}$ draws on information gained from ob
 servations made at the locations sampled at preceding time points $t_1\, l
 dots\, t_n$. A Bayesian design selection criterion is developed and its pe
 rformance is evaluated using several examples. \n
LOCATION:Seminar Room 1\, Newton Institute
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