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Bayesian Estimation of Augmented Exponential Strength Reliability Models Under Non-informative Priors



N. Chandra and V. K. Rathaur


In this article, the augmented strength reliability models are derived by assuming that the Inverse Gaussian stress(Y) is subjected to equipment having exponential strength(X) and are independent to each other.In a real life situations many manufactured new equipments/ products are being failed completely or partially at very early stage of its use, due to lack of its strength. Hence, ASP is proposed to protect such types of failures. The maximum likelihood (ML) and Bayes estimation of augmented strength reliability are considered. In Bayesian paradigm the non-informative types (uniform and Jeffrey’s) priors are chosen under symmetric and asymmetric loss functions for better comprehension. A comparison between the ML and Bayes estimators of augmented strength reliability is carried out on the basis of their mean square errors (mse) by simulating Monte-Carlo samples from posterior distribution by using Metropolis-Hasting approximation.


Stress-Strength reliability, Augmentation, Exponential Distribution, Inverse Gaussian distribution, Metropolis-Hasting Algorithm.


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