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Inference in Bayesian Networks using Dynamic Discretisation

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We present a new approximate inference algorithm for use in hybrid Bayesian Networks (BNs). The algorithm efficiently combines dynamic discretisation with robust propagation algorithms on junction trees structures. Our approach offers a significant extension to Bayesian Network theory 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 algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily.

We show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, 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), Fast Fourier Transforms and others.

This talk is part of the Inference Group series.

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