论文标题
通过$ f $ diverences在估算程序中贝叶斯风险的较低限制
Lower-bounds on the Bayesian Risk in Estimation Procedures via $f$-Divergences
论文作者
论文摘要
我们考虑贝叶斯环境中参数估计的问题，并提出了一个普通的下限，其中包括$ f $ divergences家族的一部分。然后将结果应用于感兴趣的特定设置，并将其与文献中其他值得注意的结果进行了比较。特别是，我们表明，使用相互信息的已知界限可以通过使用，例如最大泄漏，Hellinger Divergence或曲棍球粘性发散的概括。
We consider the problem of parameter estimation in a Bayesian setting and propose a general lower-bound that includes part of the family of $f$-Divergences. The results are then applied to specific settings of interest and compared to other notable results in the literature. In particular, we show that the known bounds using Mutual Information can be improved by using, for example, Maximal Leakage, Hellinger divergence, or generalizations of the Hockey-Stick divergence.
