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Alan Marrs

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NeurIPS Conference 1998 Conference Paper

Exploratory Data Analysis Using Radial Basis Function Latent Variable Models

  • Alan Marrs
  • Andrew Webb

Two developments of nonlinear latent variable models based on radial basis functions are discussed: in the first, the use of priors or constraints on allowable models is considered as a means of preserving data structure in low-dimensional representations for visualisation purposes. Also, a resampling approach is introduced which makes more effective use of the latent samples in evaluating the likelihood.

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NeurIPS Conference 1997 Conference Paper

An Application of Reversible-Jump MCMC to Multivariate Spherical Gaussian Mixtures

  • Alan Marrs

Applications of Gaussian mixture models occur frequently in the fields of statistics and artificial neural networks. One of the key issues arising from any mixture model application is how to es(cid: 173) timate the optimum number of mixture components. This paper extends the Reversible-Jump Markov Chain Monte Carlo (MCMC) 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 param(cid: 173) eter of the model which we shall estimate. The Reversible-Jump MCMC algorithm is capable of moving between parameter sub(cid: 173) spaces which correspond to models with different numbers of mix(cid: 173) ture components. As a result a sample from the full joint distribu(cid: 173) tion of all unknown model parameters is generated. The technique is then demonstrated on a simulated example and a well known vowel dataset.

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