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We propose a method to fuse posterior distributions learned from heterogeneous datasets.
The Hungarian method for the assignment problem
Kuhn, H. W · 1955
Earlier work this paper cites.
Objective criteria for the evaluation of clustering methods
Rand, W. M · 1971
Earlier work this paper cites.
A Bayesian analysis of some nonparametric problems
Ferguson, T. S · 1973
Earlier work this paper cites.
Bayesian Learning for Neural Networks
Neal, R. M · 1995
Earlier work this paper cites.
A Bayesian approach to on-line learning
Opper, M · 1998
Earlier work this paper cites.
A variational Bayesian framework for graphical models
Attias, H · 2000
Earlier work this paper cites.
Expectation propagation for approximate Bayesian inference
Minka, T. P · 2001
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Bishop, C. M · 2006
Earlier work this paper cites.
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