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Abstract:
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
methods are demonstrated which can be used to sample from the
posterior distribution over trees containing variable numbers of
hidden units.
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