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SUMMARY:Optimal experimental design for stochastic population models - Pag
 endam\, D (CSIRO Mathematics\, Informatics and Statistics)
DTSTART:20110718T160000Z
DTEND:20110718T163000Z
UID:TALK32072@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Markov population processes are popular models for studying a 
 wide range of phenomena including the spread of disease\, the evolution of
  chemical reactions and the movements of organisms in population networks 
 (metapopulations). Our ability to use these models can effectively be limi
 ted by our knowledge about parameters\, such as disease transmission and r
 ecovery rates in an epidemic. Recently\, there has been interest in devisi
 ng optimal experimental designs for stochastic models\, so that practition
 ers can collect data in a manner that maximises the precision of maximum l
 ikelihood estimates of the parameters for these models. I will discuss som
 e recent work on optimal design for a variety of population models\, begin
 ning with some simple one-parameter models where the optimal design can be
  obtained analytically and moving on to more complicated multi-parameter m
 odels in epidemiology that involve latent states and non-exponentially dis
 tributed infectious periods. For these more complex models\, the optimal d
 esign must be arrived at using computational methods and we rely on a Gaus
 sian diffusion approximation to obtain analytical expressions for the Fish
 er information matrix\, which is at the heart of most optimality criteria 
 in experimental design. I will outline a simple cross-entropy algorithm th
 at can be used for obtaining optimal designs for these models. We will als
 o explore some recent work on optimal designs for population networks with
  the aim of estimating migration parameters\, with application to avian me
 tapopulations.\n
LOCATION:Seminar Room 1\, Newton Institute
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