A MCMC Approach to Hierarchical Mixture Modelling

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Christopher Williams


There are many hierarchical clustering algorithms available, but these lack a firm statistical basis. Here we set up a hierarchical probabilistic mixture model, where data is generated in a hierarchical tree-structured manner. Markov chain Monte Carlo (MCMC) methods are demonstrated which can be used to sample from the posterior distribution over trees containing variable numbers of hidden units.