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参数比较的贝叶斯与经验贝叶斯方法AI 解读

The number of successes out of the total number of trials is observed for each of a set of N independent binomial experiment's and the associated success probabilities are compared. Ranking procedures for comparing these success probabilities are constructed under circumstances where the number of trials for each of the experiments may differ by a large amount. A loss function is constructed that represents an appropriate measure of distance between a given ranking of the…

参数比较的贝叶斯与经验贝叶斯程序AI 解读

The number of successes out of the total number of trials is observed for each of a set of N independent binomial experiment's and the associated success probabilities are compared. Ranking procedures for comparing these success probabilities are constructed under circumstances where the number of trials for each of the experiments may differ by a large amount. A loss function is constructed that represents an appropriate measure of distance between a given ranking of the…

多元总体选择的贝叶斯与极小极大样本可容许程序AI 解读

Let P be a class of independent populations each having an underlying multivariate normal distribution with unknown mean vector. Independent vector samples of common size n are drawn from each population in P. We say that a subset G of P is δ*-Pareto-optimal (in P) if no other population in P has the property that each component of its mean vector is larger by at least δ* ≥ 0 than the corresponding component of the mean vector of any given population in G. This article…

多元总体选择的贝叶斯与极小极大样本可容许程序AI 解读

Let P be a class of independent populations each having an underlying multivariate normal distribution with unknown mean vector. Independent vector samples of common size n are drawn from each population in P. We say that a subset G of P is δ*-Pareto-optimal (in P) if no other population in P has the property that each component of its mean vector is larger by at least δ* ≥ 0 than the corresponding component of the mean vector of any given population in G. This article…