基于右截尾数据的熵和熵估计:一种贝叶斯非参数方法

IF 0.8 Q3 STATISTICS & PROBABILITY

Monte Carlo Methods and Applications Pub Date : 2022-09-30 DOI:10.1515/mcma-2022-2123

L. Al-Labadi, Muhammad Tahir

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引用次数: 0

摘要

熵和熵是信息论的核心度量。本文提出了含有可能正确截尾数据的熵和熵的贝叶斯非参数估计。该方法使用了beta-Stacy过程和差分运算符。举例说明了该估计器的性能。

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Estimation of entropy and extropy based on right censored data: A Bayesian non-parametric approach

Abstract Entropy and extropy are central measures in information theory. In this paper, Bayesian non-parametric estimators to entropy and extropy with possibly right censored data are proposed. The approach uses the beta-Stacy process and the difference operator. Examples are presented to illustrate the performance of the estimators.

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来源期刊

Monte Carlo Methods and Applications STATISTICS & PROBABILITY-

CiteScore

1.20

自引率

22.20%

发文量