Parameter Estimation for Composite Dynamical Systems Based on Sequential Order Statistics from Burr Type XII Mixture Distribution

Considering the impact of heterogeneous conditions mixture baseline distribution on parameter estimation a composite dynamical system (CDS), we propose an approach to infer model parameters and survival function CDS using maximum likelihood Bayesian methods. The power-trend hazard rate Burr type XII as are used characterize changes residual lifetime surviving components. Markov chain Monte Carlo via new Metropolis–Hastings within Gibbs sampling algorithm is proposed overcome computation complexity when obtaining Bayes estimates parameters. A numerical example generated from analyze procedure. simulations conducted investigate performance methods, results show that method outperforms obtain reliable in terms bias mean square error.