Modeling of System Availability and Bayesian Analysis of Bivariate Distribution

To meet the desired standard, it is important to monitor and analyze different engineering processes to obtain the desired output. The bivariate distributions have received a significant amount of attention in recent years due to their ability to describe randomness of natural as well as artificial...

Full description

Saved in:
Bibliographic Details
Published in:Symmetry (Basel) 2023-09, Vol.15 (9), p.1698
Main Authors: Farooq, Muhammad, Gul, Ahtasham, Alshanbari, Huda M., Khosa, Saima K.
Format: Article
Language:English
Subjects:
Aging
Availability
Bayesian
Bayesian analysis
Bivariate analysis
Component reliability
Failure times
Hydrology
Markov chains
Markov processes
maximum likelihood estimation
Monte Carlo Markov Chain
Random variables
reliability
Repair time
Skewed distributions
system availability
Weibull Inverse Lomax
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To meet the desired standard, it is important to monitor and analyze different engineering processes to obtain the desired output. The bivariate distributions have received a significant amount of attention in recent years due to their ability to describe randomness of natural as well as artificial mechanisms. In this article, a bivariate model is constructed by compounding two independent asymmetric univariate distributions and by using the nesting approach to study the effect of each component on reliability for better understanding. Furthermore, the Bayes analysis of system availability is studied by considering prior parametric variations in the failure time and repair time distributions. Basic statistical characteristics of marginal distribution like mean median and quantile function are discussed. We used inverse Gamma prior to study its frequentist properties by conducting a Monte Carlo Markov Chain (MCMC) sampling scheme.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym15091698