Dependent Normalized Random Measures
Date
2013
Authors
Chen, Changyou
Rao, Vinayak
Buntine, Wray
Teh, Yee Whye
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MIT Press
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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The Sample-Complexity of General Reinforcement Learning
Type
Conference paper
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2037-12-31
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