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ObservationType
- Observations from the ConditionalType that are used to estimate the
parameters of the distribution.ParameterType
- Type of parameter estimated by this algorithm, which is used to
parameterize the conditional distribution.PosteriorType
- Type of posterior Distribution, which describes the uncertainty of the
parameters after we have incorporated the observations.@PublicationReferences(references={@PublicationReference(author="William M. Bolstad",title="Introduction to Bayesian Statistics: Second Edition",type=Book,year=2007,notes="Good introductory text."),@PublicationReference(author="Christian P. Robert",title="The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, Second Edition",type=Book,year=2007,notes="Good advanced text."),@PublicationReference(author="Wikipedia",title="Bayes estimator",type=WebPage,year=2009,url="http://en.wikipedia.org/wiki/Bayes_estimator")}) public interface BayesianEstimator<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>>
A type of estimation procedure based on Bayes's rule, which allows us to estimate the uncertainty of parameters given a set of observations that we are given.
Method Summary |
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Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner |
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learn |
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable |
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clone |
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