scholarly journals The extended Kullback-Leibler divergence measure in the unknown probability density function cases and applications

2021 ◽  
Vol 14 (4) ◽  
pp. 403
Author(s):  
Hoa Le ◽  
Hoang Van Truong ◽  
Pham The Bao
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Divergence Measure ◽  
Unknown Probability ◽  
Leibler Divergence

Anomaly detection based on probability density function with Kullback–Leibler divergence

2016 ◽  
Vol 126 ◽  
pp. 12-17 ◽  
Author(s):  
Wei Wang ◽  
Baoju Zhang ◽  
Dan Wang ◽  
Yu Jiang ◽  
Shan Qin ◽  
...  
Keyword(s):  
Probability Density Function ◽  
Anomaly Detection ◽  
Probability Density ◽  
Density Function ◽  
Leibler Divergence

Some Properties of Residual R-norm Entropy and Divergence Measures

2017 ◽  
Vol 69 (2) ◽  
pp. 205-221
Author(s):  
M. N. Linu ◽  
S. M. Sunoj
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Present Article ◽  
Shannon Entropy ◽  
Random Variable ◽  
Survival Models ◽  
Divergence Measure ◽  
Conditional Survival ◽  
Reliability Modelling

Shannon entropy plays an important role in measuring the expected uncertainty contained in the probability density function about the predictability of an outcome of a random variable. However, in certain systems, Shannon entropy may not be appropriate, where some generalized versions of it are only suitable. One such generalization is due to Boekee and Lubee [1] , called R-norm entropy. Recently, Nanda and Das [2] studied the R-norm entropy and its divergence measure in the context of used items, useful in reliability modelling. In the present article, we further study R-norm entropy and divergence in the context of weighted models. We also extend these measures to the conditionally specified and conditional survival models, and studied their properties.

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