## The nested chinese restaurant process and bayesian inference of topic hierarchies (2007)

### Cached

### Download Links

Citations: | 53 - 9 self |

### BibTeX

@MISC{Blei07thenested,

author = {David M. Blei and Thomas L. Griffiths and Michael I. Jordan},

title = {The nested chinese restaurant process and bayesian inference of topic hierarchies},

year = {2007}

}

### OpenURL

### Abstract

We present the nested Chinese restaurant process (nCRP), a stochastic process which assigns probability distributions to infinitely-deep, infinitely-branching trees. We show how this stochastic process can be used as a prior distribution in a Bayesian nonparametric model of document collections. Specifically, we present an application to information retrieval in which documents are modeled as paths down a random tree, and the preferential attachment dynamics of the nCRP leads to clustering of documents according to sharing of topics at multiple levels of abstraction. Given a corpus of documents, a posterior inference algorithm finds an approximation to a posterior distribution over trees, topics and allocations of words to levels of the tree. We demonstrate this algorithm on collections of scientific abstracts from several journals. This model exemplifies a recent trend in statistical machine learning—the use of Bayesian nonparametric methods to infer distributions on flexible data structures.