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We consider the problem of regression on multivariate count data and present a Gibbs sampler for a latent feature regression model suitable for both under-and overdispersed response variables. The model learns count-valued latent features conditional on arbitrary covariates, modeling them as negative binomial variables, and maps them into the dependent(More)
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