Asymptotically minimax Bayes predictive densities
渐近极小极大贝叶斯预测密度
Given a random sample from a distribution with density function that depends on an unknown parameter θ, we are interested in accurately estimating the true parametric density function at a future observation from the same distribution. The asymptotic risk of Bayes predictive density estimates with Kullback–Leibler loss function D(fθ||{f̂})=∫fθ log(fθ/f̂) is used to examine various ways of choosing prior distributions; the principal type of choice studied is minimax. We seek…