We conduct a detailed investigation of tempered posteriors and uncover a number of crucial and previously undiscussed points. Contrary to previous results, we first show that for realistic models and datasets and the tightly controlled case of the Laplace approximation to the posterior, stochasticity does not in general improve test accuracy. The coldest temperature is often optimal. One might think that Bayesian models with some stochasticity can at least obtain improvements in terms of calibration. However, we show empirically that when gains are obtained this comes at the cost of degradation in test accuracy. We then discuss how targeting Frequentist metrics using Bayesian models provides a simple explanation of the need for a temperature parameter $\lambda$ in the optimization objective. Contrary to prior works, we finally show through a PAC-Bayesian analysis that the temperature $\lambda$ cannot be seen as simply fixing a misspecified prior or likelihood.

我们对调和后验进行了详细研究，揭示了许多关键但以前未讨论过的问题。与以往结果相反，我们首先证明，在逼真的模型和数据集以及对后验的紧密控制情况下，随机性一般情况下不会提高测试准确性。最低温度通常是最优的。人们可能认为，带有某些随机性的贝叶斯模型至少可以在校准方面获得改进。然而，我们通过实证研究表明，当获得增益时，这是以降低测试准确性的代价为代价的。然后，我们讨论了使用贝叶斯模型来定位频率主义指标的需求的最优温度参数λ的优化目标的一个简单解释。与之前的作品相反，最后我们通过PAC-Bayesian分析表明，温度参数λ不能简单地被视为修正了先验或似然的错误设置。

钢化后座椅的细节