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]|
|Date of publication||2016-12-01 19:44:32|
|Maintainer||Balasubramanian Narasimhan <naras@stat.Stanford.EDU>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.