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See:
Description
Class Summary | |
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DoesNothingLogPrior | This class defines a LogPrior that does not penalize any parameter. |
LogPrior | The abstract class for any log-prior used e.g. for maximum supervised posterior optimization. |
SeparateGaussianLogPrior | Class for a LogPrior that defines a Gaussian prior on the parameters of a set of NormalizableScoringFunction s and a set of class-parameters. |
SeparateLaplaceLogPrior | Class for a LogPrior that defines a Laplace-prior on the parameters of a set of NormalizableScoringFunction s and a set of class-parameters. |
SeparateLogPrior | Abstract class for priors that penalize each parameter value independently and have some variance (and possible mean) as hyper-parameters. |
SimpleGaussianSumLogPrior | This class implements a prior that is a product of Gaussian distributions with mean 0 and equal variance for each parameter. |
Provides a general definition of a parameter log-prior and a number of implementations of Laplace and Gaussian priors.
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