Comments on "Fuzzy Set Theory versus Bayesian Statistics"

1978 ◽  
Vol 8 (4) ◽  
pp. 332-333 ◽  
Author(s):  
Ramesh Jain ◽  
William Stallings
2016 ◽  
Vol 32 (3) ◽  
pp. 239-257 ◽  
Author(s):  
S. Mahmoud Taheri

After introducing and developing fuzzy set theory, a lot of studies have been done to combine statistical methods and fuzzy set theory. Thisworks, called fuzzy statistics, have been developed in some branches.In this article we review essential works on fuzzy estimation, fuzzy hypotheses testing, fuzzy regression, fuzzy Bayesian statistics, and some relevant fields.


Author(s):  
S. Vadde ◽  
S. Swadi ◽  
N. Bhattacharya ◽  
F. Mistree ◽  
J. K. Allen

Abstract During the early stages of project initiation, the information available to a designer may be uncertain (imprecise or stochastic). In response to this need, two extensions of the crisp compromise Decision Support Problem using fuzzy set theory and Bayesian statistics are developed to model uncertainty in design problems. The fuzzy compromise DSP is used to model imprecise information and the Bayesian compromise DSP is used to model stochastic information. The design of an aircraft tire is used as an illustrative example.


2020 ◽  
Vol 265 ◽  
pp. 121779 ◽  
Author(s):  
Luiz Maurício Furtado Maués ◽  
Brisa do Mar Oliveira do Nascimento ◽  
Weisheng Lu ◽  
Fan Xue

2020 ◽  
Vol 38 (4) ◽  
pp. 3971-3979
Author(s):  
Yana Yuan ◽  
Huaqi Chai

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