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» Dirichlet Process Mixtures of Generalized Linear Models
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JMLR
2011
148views more  JMLR 2011»
14 years 6 months ago
Bayesian Generalized Kernel Mixed Models
We propose a fully Bayesian methodology for generalized kernel mixed models (GKMMs), which are extensions of generalized linear mixed models in the feature space induced by a repr...
Zhihua Zhang, Guang Dai, Michael I. Jordan
AAAI
2010
15 years 1 months ago
A Bayesian Nonparametric Approach to Modeling Mobility Patterns
Constructing models of mobile agents can be difficult without domain-specific knowledge. Parametric models flexible enough to capture all mobility patterns that an expert believes...
Joshua Mason Joseph, Finale Doshi-Velez, Nicholas ...
NIPS
1993
15 years 1 months ago
Mixtures of Controllers for Jump Linear and Non-Linear Plants
We describe an extension to the Mixture of Experts architecture for modelling and controlling dynamical systems which exhibit multiple modesof behavior. This extension is based on...
Timothy W. Cacciatore, Steven J. Nowlan
SIGIR
2008
ACM
14 years 11 months ago
A new probabilistic retrieval model based on the dirichlet compound multinomial distribution
The classical probabilistic models attempt to capture the Ad hoc information retrieval problem within a rigorous probabilistic framework. It has long been recognized that the prim...
Zuobing Xu, Ram Akella
ICIP
2004
IEEE
16 years 1 months ago
Sparse representation of images with hybrid linear models
We propose a mixture of multiple linear models, also known as hybrid linear model, for a sparse representation of an image. This is a generalization of the conventional KarhunenLo...
Kun Huang, Allen Y. Yang, Yi Ma