几乎相合性与Bayes相合性

ALMOST CONSISTENCY AND BAYES CONSISTENCY

  • 摘要: 本文包含两部分,首先我们证明了一个定理,它断言:在唯一的条件“不同参数值对应着不同的分布”之下,必存在一个“几乎相合”的估计,其次,基于这一结果,我们在很一般的条件下证明了当样本量趋于无穹时,一个贝叶斯决策必然是贝叶斯相合的,也就是说,若以Rn记样本量为n时贝叶斯决策的贝叶斯风险,则当n→∞时有R_n \rightarrow \inf _\theta L\left(\theta_0 a\right)这里L已为损失函数,a跑遍行动空间,而θ0为真参数值。

     

    Abstract: This paper Consists of two parts. First we prove a theorem asserting that under the sole condition, that different parameter Values correspond to different distributiona, an "almost consistent" estimate of the para meter must exist. Further based on this result, we show, under very general conditions, that as the sample size tends to infinity, a Bayesian strategy must be "Bayesian Consistent" If we denote by Rn the Baysian risk of the Bayesian strategy with sample size n, then R_n \rightarrow \inf _\theta L\left(\theta_0 a\right), where L is the loss function, a runs over the action space and θ0 is the true parameter.

     

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