- Volume 13 Issue 2
A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced mixture failure model of Rayleigh and Erlang(2) pattern. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Gibbs steps are proposed to perform the Bayesian inference of such models. For model determination, we explored sum of relative error criterion that selects the best model. A numerical example with simulated data set is given.
- IEEE Transactions on Reliability v.46 Bayes Computation for Reliability Estimation Akman, O.;Huwang, L.
- Technometrics v.12 Estimation of Parameters in Compound Weibull Distributions Falls, L. W.
- Journal of the American Statistical Association v.85 Sampling-Based Approaches to Calculating Marginal Densities Gelfand, A. E.;Smith, A. F. M.
- Statistical Science v.7 Inference from Iterative Simulation Using Multiple Sequences Gelman, A. E.;Rubin, D.
- IEE Transactions on Pattern Analysis and Machine Intelligence v.6 Stochastic Relaxation, Gibbs Distribution and the Bayesian Restoration of Images Geman, S.;Geman, D.
- Methods for Statistical Analysis of Reliability and Data Mann, N. R.;Schafer, R. E.;Singpurwalla, N. D.
- Biometrika v.45 Estimation of Parameters of Mixed Exponential Distributed Failure Times from Censored Life Test Data Mendenhall, W.;Hader, R. J.
- Software Reliability: Measurement, Prediction, Application Musa, J. D.;Iannino, A.;Okumoto, K.
- Technometrics v.10 The use of Fractional Moments for Estimating the Parameters of a Mixed Exponential Distribution Tallis, G.M.;Light, R.
- IEEE Transactions on Reliability v.46 Bayes Result for Classical Pareto Distribution via Gibbs Sampler, with Double-censored Observations Upadhyay, S. K.;Shastri, V.
- USER'S MANUAL STAT/LIBRARY FORTRAN Subroutines for statistical analysis v.3 IMSL