%0 Journal Article
%T A Bayesian Analysis of Perks Distribution via Markov Chain Monte Carlo Simulation
%A Arun Kumar Chaudhary
%A Vijay Kumar
%J Nepal Journal of Science and Technology
%P 153-166
%D 2013
%R 10.3126/njst.v14i1.8936
%X In this paper the Markov chain Monte Carlo (MCMC) method is used to estimate the parameters of Perks distribution based on a complete sample. The procedures are developed to perform full Bayesian analysis of the Perks distributions using MCMC simulation method in OpenBUGS. We obtained the Bayes estimates of the parameters, hazard and reliability functions, and their probability intervals are also presented. We also discussed the issue of model compatibility for the given data set. A real data set is considered for illustration under gamma sets of priors. Nepal Journal of Science and Technology Vol. 14, No. 1 (2013) 153-166 DOI: http://dx.doi.org/10.3126/njst.v14i1.8936
%K Bayesian estimation
%K Markov chain Monte Carlo
%K maximum likelihood estimation
%K model validation
%K Perks distribution
%K predictive simulation
%K openBUGS
%U http://www.nepjol.info/index.php/NJST/article/view/8936