An Approximate Probability Distribution for the order of Elements of the Symmetric Group

1980 ◽  
Vol 12 (1) ◽  
pp. 41-46 ◽  
Author(s):  
J. D. Bovey
Keyword(s):  
Probability Distribution ◽  
Symmetric Group ◽  
Approximate Probability ◽  
Order Of Elements

scholarly journals Approximation of Densities on Riemannian Manifolds

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 43 ◽  
Author(s):  
Alice Le Brigant ◽  
Stéphane Puechmorel
Keyword(s):  
Riemannian Manifold ◽  
Probability Distribution ◽  
Riemannian Manifolds ◽  
Measure Space ◽  
Parametric Estimation ◽  
Dimensional Euclidean Space ◽  
Underlying Space ◽  
Finite Dimensional ◽  
Approximate Probability ◽  
Non Parametric Estimation

Finding an approximate probability distribution best representing a sample on a measure space is one of the most basic operations in statistics. Many procedures were designed for that purpose when the underlying space is a finite dimensional Euclidean space. In applications, however, such a simple setting may not be adapted and one has to consider data living on a Riemannian manifold. The lack of unique generalizations of the classical distributions, along with theoretical and numerical obstructions require several options to be considered. The present work surveys some possible extensions of well known families of densities to the Riemannian setting, both for parametric and non-parametric estimation.

scholarly journals Characterizations of Three (2020) Introduced Discrete Distributions

2020 ◽  
pp. 609-616
Author(s):  
Shirin Nezampour ◽  
G. G. Hamedani
Keyword(s):  
Applied Sciences ◽  
Probability Distribution ◽  
Short Note ◽  
Discrete Distributions ◽  
Approximate Probability

The problem of characterizing a probability distribution is an important problem which has attracted the attention of many researchers in the recent years. To understand the behavior of the data obtained through a given process, we need to be able to describe this behavior via its approximate probability law. This, however, requires to establish conditions which govern the required probability law. In other words we need to have certain conditions under which we may be able to recover the probability law of the data. So, characterization of a distribution plays an important role in applied sciences, where an investigator is vitally interested to find out if their model follows the selected distribution. In this short note, certain characterizations of three recently introduced discrete distributions are presented to complete, in some way, the works ofHussain(2020), Eliwa et al.(2020) and Hassan et al.(2020).

scholarly journals Reliability Evaluation of Power Supply for More-Electric-Aircraft Based on Information Entropy

2019 ◽  
Vol 288 ◽  
pp. 02002
Author(s):  
Xuanxuan Qi ◽  
Jian’an Cao ◽  
Xiaojiao Li
Keyword(s):  
Probability Distribution ◽  
Information Entropy ◽  
Power Supply ◽  
Evaluation Model ◽  
Reliability Evaluation ◽  
Component System ◽  
Electric Aircraft ◽  
Approximate Probability ◽  
Multi Component System ◽  
Supply Reliability

In order to overcome the shortcomings of traditional power supply reliability evaluation model of multi-electric aircraft in the process of multi-component system research, this paper introduces the approximate probability distribution of multi-component system by cross-entropy, it proposes a Monte Carlo method based on information entropy to evaluate the power supply reliability of multi-electric aircraft, and obtains the approximate probability distribution by differential evolution to make the reliability evaluation. The estimated variance is approximately zero. Finally, taking an aircraft power supply system as an example, the convergence and accuracy of several reliability analysis methods are compared and analyzed. The results show the superiority of this method.

Sign in / Sign up

Export Citation Format

Share Document