An estimation procedure using Bayesian inference. Suppose that the random variables corresponding to n sample means are denoted by Ῡ1, Ῡ2,…, Ῡn. The belief is that each sample is obtained from a population having the same variance, σ2, but different means, μ1, μ2,…, μn, with the means themselves being regarded as observations from a distribution with mean μ and variance ω2. The parameters μ and ω2 are called hyperparameters and are assigned prior distributions referred to as hyperpriors.
Subjects: Probability and Statistics.