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Prior normalization
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Bayesian Field Theory Nonparametric
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Neural networks
Contents
Parameterizing priors: Hyperparameters
Subsections
Prior normalization
Adapting prior means
General considerations
Density estimation
Unrestricted variation
Regression
Adapting prior covariances
General case
Automatic relevance detection
Local masses and gauge theories
Invariant determinants
Regularization parameters
Exact posterior for hyperparameters
Integer hyperparameters
Local hyperfields
Joerg_Lemm 2001-01-21
