We present a new class of estimators of Shannon entropy for severely undersampled discrete distributions. It is based on a generalization of an estimator proposed by T. Schuermann, which itself is a generalization of an estimator proposed by myself in arXiv:physics/0307138. For a special set of parameters they are completely free of bias and have a finite variance, something with is widely believed to be impossible. We present also detailed numerical tests where we compare them with other recent estimators and with exact results, and point out a clash with Bayesian estimators for mutual information.

翻译：我们展示了一种新的香农寄生体的测算器类别,用于严重少采散散分布,其基础是T. Schuermann提议的测算器的概括化,它本身就是我本人在ArXiv提出的测算器的概括化:物理/0307138。对于一套特殊的参数来说,它们完全没有偏见,有一定的差别,这是普遍认为不可能实现的。我们还提供了详细的数字测试,把它们与其他最近的测算器进行比较,并得出准确的结果,并指出与巴耶斯估计器的冲突是为了相互提供信息。