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SUMMARY:Inference in Bayesian Networks using Dynamic Discretisation - Mart
 in Neil\, Agena Ltd &amp\; David Marquez\, Queen Mary\, University of Lond
 on
DTSTART:20070326T130000Z
DTEND:20070326T140000Z
UID:TALK6674@talks.cam.ac.uk
CONTACT:Oliver Stegle
DESCRIPTION:We present a new approximate inference algorithm for use in hy
 brid Bayesian Networks (BNs). The algorithm efficiently combines dynamic d
 iscretisation with robust propagation algorithms on junction trees structu
 res. Our approach offers a significant extension to Bayesian Network theor
 y and practice by 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. Our algo
 rithm is implemented in a commercial Bayesian Network software package\, A
 genaRisk\, which allows model construction and testing to be carried out e
 asily.\n\nWe show how the rapid convergence of the algorithm towards zones
  of high probability density\, make robust inference analysis possible eve
 n in situations where\, due to the lack of information in both prior and d
 ata\, robust sampling becomes infeasible. Generated solutions to realistic
  modelling problems will be presented and compared with solutions produced
  using competing techniques such as Monte Carlo Markov Chains (MCMC)\, Fas
 t Fourier Transforms and others.\n
LOCATION:TCM Seminar Room\, Cavendish Laboratory\, Department of Physics
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