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Abstract:
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Bayesian nonparametrics provides a flexible, model-based, predictive framework for multivariate non-Gaussian density estimation and classification. However, most existing nonparametric methods assume the fundamental mixture components to be of some standard distributional form that are individually insufficient to describe the sparse non-Gaussian structures in observed data. I present a novel hierarchical mixture model, a nonparametric mixture of nonparametric mixtures, that enables automatic registration of an unknown number non-Gaussian components, each of which is itself a mixture of an unknown number of basis distributions. I will discuss inferential methods capable of exploiting high performance computing clusters, and apply the methodology to assess treatment efficacy in an adjuvant vaccine trial flow cytometry data set.
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