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Abstract:
Applications of Gaussian mixture models occur frequently in
the fields of of statistics and artificial neural networks. One of
the key issues arising from any mixture model application is how to
estimate the optimum number of mixture components. This paper
extends the Reversible-Jump Monte Carlo Markov Chain algorithm to
the case of multivariate spherical Gaussian mixtures using a
hierarchical prior model. Using this method the number of mixture
components is no longer fixed but becomes a parameter of the model
which we shall estimate. The reversible-jump MCMC algorithm is
capable of moving between parameter subspaces which correspond to
models with different numbers of mixture components. As a result a
sample from the full joint distribution of all unknown model
parameters is generated. The technique is then demonstrated on a
well known classification problem.
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