Bayesian learning of joint distributions of objects

  title={Bayesian learning of joint distributions of objects},
  author={Anjishnu Banerjee and Jared Murray and David B. Dunson},
There is increasing interest in broad application areas in defining flexible joint models for data having a variety of measurement scales, while also allowing data of complex types, such as functions, images and documents. We consider a general framework for nonparametric Bayes joint modeling through mixture models that incorporate dependence across data types through a joint mixing measure. The mixing measure is assigned a novel infinite tensor factorization (ITF) prior that allows flexible… CONTINUE READING