A MCMC Approach to Hierarchical Mixture Modelling.

Christopher K.I. Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 12 (NIPS 1999)
PublisherMIT Press
Pages680-686
Number of pages7
Publication statusPublished - 1999

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