Bayesian learning of joint distributions of objects

9 years 12 months ago

Download jmlr.org

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 dependence in cluster allocation across data types. The ITF prior is formulated as a tensor product of stick-breaking processes. Focusing on a convenient special case corresponding to a Parafac factorization, we provide basic theory justifying the ﬂexibility of the proposed prior and resulting asymptotic properties. Focusing on ITF mixtures of product kernels, we develop a new Gibbs sampling algorithm for routine implementation relying on slice sampling. The methods are compared with alterna...

Anjishnu Banerjee, Jared Murray, David B. Dunson

Real-time Traffic