The Odd Log-Logistic Generalized Gompertz Distribution: Properties, Applications and Different Methods of Estimation

Alizadeh, Morad; Benkhelifa, Lazhar; Rasekhi, Mahdi; Hosseini, Bistoon

doi:10.1007/s40304-018-00175-y

Cited by 9 publications

(1 citation statement)

References 16 publications

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“…In the study of Shahrastani [4], the E-Bayesian and hierarchical Bayesian of the scalar parameter of a Gompertz distribution under Type II censoring schemes was estimated based on fuzzy data. Alizadeh et al [5] used the Bayesian method to obtain estimates of Gompertz distribution parameters under three different loss functions. Bayesian inference requires to calculate the posterior estimators, usually expressed as complex multidimensional integrals, which are difficult to find in most practical applications.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inference for ammunition demand based on Gompertz distribution

Zhao

Shi

Qian³

et al. 2020

J. of Syst. Eng. Electron.

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Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict, combined with the law of ammunition consumption under different damage grades, a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed. The Bayesian inference model based on Gompertz distribution is constructed, and the system contribution degree is introduced to determine the weight of the multi-source information. In the case where the prior distribution is known and the distribution of the field data is unknown, the consistency test is performed on the prior information, and the consistency test problem is transformed into the goodness of the fit test problem. Then the Bayesian inference is solved by the Markov chain-Monte Carlo (MCMC) method, and the ammunition demand under different damage grades is gained. The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.

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Section: Introductionmentioning

confidence: 99%