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CATEGORIES:Inference Group
SUMMARY:Inference in Bayesian Networks using Dynamic Discr
 etisation - Martin Neil\, Agena Ltd &amp\; David M
 arquez\, Queen Mary\, University of London
DTSTART;TZID=Europe/London:20070326T140000
DTEND;TZID=Europe/London:20070326T150000
UID:TALK6674AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/6674
DESCRIPTION:We present a new approximate inference algorithm f
 or use in hybrid Bayesian Networks (BNs). The algo
 rithm efficiently combines dynamic discretisation 
 with robust propagation algorithms on junction tre
 es structures. Our approach offers a significant e
 xtension to Bayesian Network theory and practice b
 y offering a flexible way of modelling continuous 
 nodes in BNs conditioned on complex configurations
  of evidence and intermixed with discrete nodes as
  both parents and children of continuous nodes. Ou
 r algorithm is implemented in a commercial Bayesia
 n Network software package\, AgenaRisk\, which all
 ows model construction and testing to be carried o
 ut easily.\n\nWe show how the rapid convergence of
  the algorithm towards zones of high probability d
 ensity\, make robust inference analysis possible e
 ven in situations where\, due to the lack of infor
 mation in both prior and data\, robust sampling be
 comes infeasible. Generated solutions to realistic
  modelling problems will be presented and compared
  with solutions produced using competing technique
 s such as Monte Carlo Markov Chains (MCMC)\, Fast 
 Fourier Transforms and others.\n
LOCATION:TCM Seminar Room\, Cavendish Laboratory\, Departme
 nt of Physics
CONTACT:Oliver Stegle
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