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Posterior and predictive inferences for Marshall Olkin bivariate Weibull distribution via Markov chain Monte Carlo methods

  • Rakesh RanjanEmail author
  • Vastoshpati Shastri
Original Article
  • 13 Downloads

Abstract

This paper deals with the well known bi-variate Weibull distribution developed by Marshall and Olkin. In the light of prior information, this paper derives the posterior distribution and performs Markov chain Monte Carlo methods to obtain posterior based inferences. This paper also checks the sensitivity of posterior estimates by changing the prior variances followed by Bayesian prediction using sample-based approaches. Numerical illustrations are provided for real as well as simulated data sets.

Keywords

Bivariate model ML Estimates MCMC Gibbs sampler Metropolis algorithm Predictive simulation 

Notes

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Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019

Authors and Affiliations

  1. 1.DST-Centre for Interdisciplinary Mathematical SciencesBanaras Hindu UniversityVaranasiIndia
  2. 2.Department of StatisticsGovernment Arts & Science CollegeRatlamIndia

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