摘要：Although continuous density estimation has received abundant attention in the Bayesian nonparametrics literature, there is limited theory on multivariate mixed scale density estimation. In this note, we consider a general framework to jointly model continuous, count and categorical variables under a nonparametric prior, which is induced through rounding latent variables having an unknown density with respect to Lebesgue measure. For the proposed class of priors, we provide sufficient conditions for large support, strong consistency and rates of posterior contraction. These conditions allow one to convert sufficient conditions obtained in the setting of multivariate continuous density estimation to the mixed scale case. To illustrate the procedure, a rounded multivariate nonparametric mixture of Gaussians is introduced and applied to a crime and communities dataset.
关键词：large support; mixed discrete and continuous; nonparametric Bayes; rate of posterior contraction; strong posterior consistency
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