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

IF 0.8 Q3 STATISTICS & PROBABILITY
L. Al-Labadi, Muhammad Tahir
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引用次数: 0

Abstract

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.
基于右截尾数据的熵和熵估计:一种贝叶斯非参数方法
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Monte Carlo Methods and Applications
Monte Carlo Methods and Applications STATISTICS & PROBABILITY-
CiteScore
1.20
自引率
22.20%
发文量
31
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