Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical Bayes methods.
|Author||Bradley Efron [aut], Balasubramanian Narasimhan [aut, cre]|
|Maintainer||Balasubramanian Narasimhan <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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