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SUMMARY:Monte Carlo adjusted profile likelihood\, with applications to spa
 tiotemporal and phylodynamic inference. - Edward Ionides (University of Mi
 chigan)
DTSTART:20180628T104500Z
DTEND:20180628T113000Z
UID:TALK107488@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Partially observed nonlinear stochastic dynamic systems raise 
 inference challenges. Sequential Monte Carlo (SMC) methods provide a route
  to accessing the likelihood function. However\, despite the advantage of 
 applicability to a wide class of nonlinear models\, standard SMC methods h
 ave a limitation that they scale poorly to large systems. We present a pro
 file likelihood approach\, properly adjusted for Monte Carlo uncertainty\,
  that enables likelihood-based inference in systems for which Monte Carlo 
 error remains large despite stretching the limits of available computation
 al resources. Together with state-of-the-art SMC algorithms\, this techniq
 ue permits effective inference on some scientific problems in panel time s
 eries analysis\, spatiotemporal modeling\, and inferring population dynami
 c models from genetic sequence data.  The results presented are joint work
  with Carles Breto\, Joonha Park\, Alex Smith and Aaron King.
LOCATION:Seminar Room 1\, Newton Institute
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