Marginal Likelihood Maximization is an estimation method in empirical Bayes that belongs to the class of likelihood-based inference techniques, distinguished by its maximization of the integrated likelihood obtained by marginalizing over latent parameters with respect to a prior distribution.
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Type-ii Maximum Likelihood
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Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu et al. · arXiv (Cornell University) · 2015 · 1.3K citations · Full text
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University of Amsterdam
Amsterdam, The Netherlands
15 h-index
44 publications
4.1K citations
University College London
London, United Kingdom
13 h-index
33 publications
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University of Cambridge
Cambridge, United Kingdom
11 h-index
22 publications
1.6K citations
University of Wisconsin–Madison
Madison, United States
10 h-index
20 publications
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Seattle, United States
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25 publications
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Carnegie Mellon University
Pittsburgh, United States
9 h-index
24 publications
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University of Padua
Padua, Italy
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14 publications
803 citations
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Sheffield, United Kingdom
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13 publications
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Repository · 64 publications · 3.9K citations
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15 publications · 2.2K citations
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12 publications · 1.1K citations
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