Dependent Normalized Random Measures
Changyou Chen,Vinayak Rao,Wray Buntine,Yee Whye Teh +3 more
- 16 Jun 2013
- pp 969-977
TL;DR: It is shown that both MNRM and TNRM are marginally normalized random measures, resulting in well understood theoretical properties and in time-varying topic modeling experiments, both models exhibit superior performance over related dependent models.
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Abstract: In this paper we propose two constructions of dependent normalized random measures, a class of nonparametric priors over dependent probability measures. Our constructions, which we call mixed normalized random measures (MNRM) and thinned normalized random measures (TNRM), involve (respectively) weighting and thinning parts of a shared underlying Poisson process before combining them together. We show that both MNRM and TNRM are marginally normalized random measures, resulting in well understood theoretical properties. We develop marginal and slice samplers for both models, the latter necessary for inference in TNRM. In time-varying topic modeling experiments, both models exhibit superior performance over related dependent models such as the hierarchical Dirichlet process and the spatial normalized Gamma process.
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