Hierarchical bayesian model
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In a standard Bayesian model, the parameters are drawn from prior distributions, the parameters of which are fixed by the modeller. In a hierarchical model, these parameters, usually referred to as hyperparameters, are also free to vary and are themselves drawn from priors, often referred to as hyperpriors. This form of modelling is most useful for data that is composed of exchangeable groups, such as genes, for which the possibility is required that the parameters that describe each group might or might not be the same. (Beaumont 2004)
