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In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis. For example, if one is using a beta distribution to model the distribution of the parameter ''p'' of a Bernoulli distribution, then: * ''p'' is a parameter of the underlying system (Bernoulli distribution), and * ''α'' and ''β'' are parameters of the prior distribution (beta distribution), hence ''hyper''parameters. One may take a single value for a given hyperparameter, or one can iterate and take a probability distribution on the hyperparameter itself, called a hyperprior. == Purpose == One often uses a prior which comes from a parametric family of probability distributions – this is done partly for explicitness (so one can write down a distribution, and choose the form by varying the hyperparameter, rather than trying to produce an arbitrary function), and partly so that one can ''vary'' the hyperparameter, particularly in the method of ''conjugate priors,'' or for ''sensitivity analysis.'' 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Hyperparameter」の詳細全文を読む スポンサード リンク
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