Title: Auxiliary Variables for Multi-Dirichlet Priors,
Abstract: Bayesian models that mix multiple Dirichlet prior parameters, called
Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring
mixing weights and parameters of mixed prior distributions seems tricky, as
sums over Dirichlet parameters complicate the joint distribution of model
parameters.
This paper shows a novel auxiliary variable scheme which helps to simplify
the inference for models involving hierarchical MDs and MDPs. Using this
scheme, it is easy to derive fully collapsed inference schemes which allow for
an efficient inference.