Abstract A Bayesian model may be relied on to the extent of its adequacy by minimizing the posterior expected loss raised to the power of a discounting exponent. The resulting action is minimax under broad conditions when the sample size is held fixed and the discounting exponent is infinite. On the other hand, for any finite discounting exponent, the action is Bayes when the sample size is sufficiently large. Thus, the action is Bayes when there is enough reliable information in the posterior distribution, is minimax when the posterior distribution is completely unreliable, and is a continuous blend of the two extremes otherwise.

中文翻译：

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在后验分布不可靠的情况下，从贝叶斯推断转向极小极大

摘要 贝叶斯模型可以在其充分性的范围内通过最小化后验期望损失来依赖于折扣指数的幂。当样本量保持固定且贴现指数是无限的时，在广泛条件下产生的作用是极大极小。另一方面，对于任何有限折扣指数，当样本量足够大时，动作是贝叶斯。因此，当后验分布中有足够的可靠信息时，动作是贝叶斯，当后验分布完全不可靠时，动作是极小极大，否则是两个极端的连续混合。