Hyperparameter
Bayesian inference, Prior probability, Conjugate prior, Bernoulli distribution
978-613-5-93350-5
6135933507
56
2011-05-29
29,00 €
eng
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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. They arise particularly in the use of conjugate priors.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 hyperparameters.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.
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Teoria della probabilità, processi stocastici, statistica matematica
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