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Inference in Models with Latent Hierarchies

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Many naturally occuring datasets have underlying hierarchical structures, motivating the desire for probabilistic models to capture and exploit these properties. In this talk, I will present three things:
  1. a model which uses multiple concurrent hierarchies as its latent data structure,
  2. a method of making MCMC inference for such models much faster,
  3. plots showing the predictive performance of this model

This talk is part of the Inference Group series.

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