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>).
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curl https://depscope.dev/api/check/conda/r-deconvolverFirst published · 2025-06-24 16:25:23.809000+00:00
Last updated · 2025-09-23 13:22:42.275000+00:00