@article {citation209,
	title = {Hierarchical Dirichlet processes},
	journal = {Journal of the American Statistical Association},
	volume = {101},
	year = {2006},
	month = {11/2005},
	pages = {1566-1581},
	abstract = {We consider problems involving groups of data, where each observation within a group is 
a draw from a mixture model, and where it is desirable to share mixture components between 
groups. We assume that the number of mixture components is unknown a priori and is to be 
inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one 
for each group, where the well-known clustering property of the Dirichlet process provides a 
nonparametric prior for the number of mixture components within each group. Given our desire 
to tie the mixture models in the various groups, we consider a hierarchical model, specifically 
one in which the base measure for the child Dirichlet processes is itself distributed according to 
a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessar- 
ily share atoms. Thus, as desired, the mixture models in the different groups necessarily share 
mixture components. We discuss representations of hierarchical Dirichlet processes in terms of 
a stick-breaking process, and a generalization of the Chinese restaurant process that we refer 
to as the {\textquotedblleft}Chinese restaurant franchise.{\textquotedblright} We present Markov chain Monte Carlo algorithms 
for posterior inference in hierarchical Dirichlet process mixtures, and describe applications to 
problems in information retrieval and text modelling. 
},
	url = {http://www.cs.berkeley.edu/~jordan/papers/hdp.pdf},
	author = {Y. W. Teh and M. I. Jordan and M. J. Beal and D. M. Blei}
}
