摘要：Empirical Bayes inference assumes an unknown prior density g(θ) has yielded (unobservables) Θ 1 , Θ 2 , ..., Θ N , and each Θ i produces an independent observation X i from p i (X i Θ i ). The marginal density f i (X i ) is a convolution of the prior g and p i . The Bayes deconvolution problem is one of recovering g from the data. Although estimation of g - so called g-modeling - is difficult, the results are more encouraging if the prior g is restricted to lie within a parametric family of distributions. We present a deconvolution approach where g is restricted to be in a parametric exponential family, along with an R package deconvolveR designed for the purpose.
