The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
Abstract. This paper studies a Bayesian framework for density modeling with mixture of exponential family distributions. Variational Bayesian Dirichlet-Multinomial allocation (VBDM...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...
Recent research has made significant progress on the problem of bounding log partition functions for exponential family graphical models. Such bounds have associated dual paramete...
Directed graphical models with one layer of observed random variables and one or more layers of hidden random variables have been the dominant modelling paradigm in many research ...
Given a model family and a set of unlabeled examples, one could either label specific examples or state general constraints--both provide information about the desired model. In g...