Description
Empirical Bayes Estimation Strategies.
Description
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>).
README.md
Empirical Bayes Deconvolution
An unknown prior density $g(\theta)$ has yielded (unobservable) $\Theta_1, \Theta_2,\ldots,\Theta_N$, and each $\Theta_i$ produces an observation $X_i$ from an exponential family. deconvolveR is an R package for estimating prior distribution $g(\theta)$ from the data using Empirical Bayes deconvolution.
Details and examples may be found in the paper by Narasimhan and Efron, 2020. A vignette with further examples is also provided.