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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. Details and examples are in the paper by Narasimhan and Efron (2020, <doi:10.18637/jss.v094.i11>).
Package details |
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Author | Bradley Efron [aut], Balasubramanian Narasimhan [aut, cre] |
Maintainer | Balasubramanian Narasimhan <naras@stat.Stanford.EDU> |
License | GPL (>= 2) |
Version | 1.2-1 |
URL | https://bnaras.github.io/deconvolveR/ |
Package repository | View on CRAN |
Installation |
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