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SUMMARY:Bayesian hierarchical models and recent computational development 
 using Integrated Nested Laplace Approximation\, with applications to pre-i
 mplantation genetic screening in IVF - Gianluca Baio\, University College\
 , London
DTSTART:20121016T133000Z
DTEND:20121016T143000Z
UID:TALK39606@talks.cam.ac.uk
CONTACT:Dr Jack Bowden
DESCRIPTION:Bayesian hierarchical models are an effective way of accountin
 g for complex structures in the data\, including clustering and nested lev
 els of information. Typically\, within a Bayesian framework\, hierarchical
  models are estimated using Markov Chain Monte Carlo methods. These are st
 andard in Bayesian analysis but\, while generally very efficient\, they ca
 n be extremely computationally intensive\, especially for hierarchical mod
 els. Recently\, alternative methods have been investigated to increase the
  computational efficiency and the precision in the estimations. In this ta
 lk\, I review the theory behind Integrated Nested Laplace Approximation\; 
 in particular\, I show an example based on data obtained at the UCL Centre
  for Pre-implantation Genetic Diagnosis\, investigating the effect of the 
 length of telomeres (a 6 base repeat found at the end of chromosomes to pr
 otect them from degradation) on chromosomal abnormalities in IVF.
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
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