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Spatial normalized gamma processes

Abstract:

Dependent Dirichlet processes (DPs) are dependent sets of random measures, each being marginally DP distributed. They are used in Bayesian nonparametric models when the usual exchangeability assumption does not hold. We propose a simple and general framework to construct dependent DPs by marginalizing and normalizing a single gamma process over an extended space. The result is a set of DPs, each associated with a point in a space such that neighbouring DPs are more dependent. We describe Mark...

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Journal:
Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
Pages:
1554-1562
Publication date:
2009-01-01
URN:
uuid:3d1860b6-7940-40f2-930c-310639018478
Source identifiers:
353243
Local pid:
pubs:353243
Language:
English

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