Variational Inference for Beta-Bernoulli Dirichlet Process Mixture Models
نویسندگان
چکیده
A commonly used paradigm in diverse application areas is to assume that an observed set of individual binary features is generated from a Bernoulli distribution with probabilities varying according to a Beta distribution. In this paper, we present our nonparametric variational inference algorithm for the Beta-Bernoulli observation model. Our primary focus is clustering discrete binary data using the Dirichlet process (DP) mixture model.
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