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- Title
Bayesian Method for the Generalized Exponential Model Using Fuzzy Data.
- Authors
Pak, Abbas; Khoolenjani, Nayereh Bagheri; Alamatsaz, Mohammad Hossein; Mahmoudi, Mohammad Reza
- Abstract
This paper focuses on Bayesian inference for the parameters of the generalized exponential model under asymmetric and symmetric loss functions when the observations are described in terms of fuzzy numbers. First, a generalized likelihood function based on fuzzy data is derived. Then, considering general entropy, linear exponential and squared error loss functions, the Bayes estimates of the parameters are obtained. Since Bayes estimates could not be expressed in closed forms, Metropolis–Hasting samplers are used to compute the approximate Bayes estimates. For comparison purposes, the maximum likelihood estimates of the parameters are also computed. The proposed inferences are illustrated using three real-world examples. The numerical simulation results demonstrate the superiority of the Bayesian method over the maximum likelihood procedure.
- Subjects
COMPUTER simulation; FUZZY numbers; MATHEMATICAL models; ENTROPY; BAYESIAN analysis
- Publication
International Journal of Fuzzy Systems, 2020, Vol 22, Issue 4, p1243
- ISSN
1562-2479
- Publication type
Article
- DOI
10.1007/s40815-020-00843-8