This paper deals with multicomponent stress–strength system reliability (MSR) and its maximum likelihood (ML) as well as Bayesian estimation. We assume that \({X}_{1},{X}_{2},\dots ,{X}_{k}\) being the random strengths of k- components of a system and Y is the applied common random stress on them, which independently follows gamma distribution with parameters \(\left({\alpha }_{1},{\lambda }_{1}\right)\) and \(\left({\alpha }_{2},{\lambda }_{2}\right)\) respectively. The system works only if \(s\left(1\le s\le k\right)\) or more of the strengths exceed the common load/stress is called s-out-of-k: G system. Maximum likelihood and asymptotic interval estimators of MSR are obtained. Bayes estimates are computed under symmetric and asymmetric loss functions assuming informative and non-informative priors. ML and Bayes estimators are numerically evaluated and compared based on mean square errors and absolute biases through simulation study employing the Metropolis–Hastings algorithm.
