Abstract: Abstract In Bayesian inference, the unknown parameter is given a prior distribution, and Bayes' theorem combines this with the likelihood from the observed data, to give the posterior distribution. The prior may be informative, subjective, or have a standard, reference form (usually representing vague knowledge). In hierarchical models, the prior distribution may have its own parameters that themselves have priors.
Publication Year: 2005
Publication Date: 2005-02-15
Language: en
Type: other
Indexed In: ['crossref']
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