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SUMMARY:Integrated Nested Laplace Approximation (INLA) - Sara Wade (Univer
 sity of Cambridge)
DTSTART:20131128T150000Z
DTEND:20131128T163000Z
UID:TALK49164@talks.cam.ac.uk
CONTACT:35825
DESCRIPTION:Integrated nested Laplace approximation (INLA) is an algorithm
  for approximate Bayesian inference in a class of latent Gaussian models. 
 This class of models is characterized by linking the possibly non-Gaussian
  outputs to the inputs through a latent Gaussian field controlled by few h
 yperparameters and includes\, among others\, generalized linear models\, a
 dditive models\, smoothing splines\, state space models\, spatial and spat
 iotemporal models\, and log-Gaussian Cox processes. The main advantage of 
 INLA over other Bayesian inference methods\, such as MCMC\, is computation
  time. In this talk\, we will describe the algorithm in detail and provide
  a demo of the R-INLA package.
LOCATION:Engineering Department\, CBL Room 438
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