eng Science and Education Publishing International Journal of Business and Risk Management 2025-11-04 6 1 11 18 10.12691/ijbrm-6-1-1 IJBRM2025611 article Wassim Abou Ghaida wassim.aboughaida@udst.edu.qa 1 Ayman Baklizi 2 Foundation Program Unit, University of Doha for Science and Technology, Doha, Qatar Department of Statistics, Yarmouk University, Irbid, Jordan We consider Bayesian inference and point prediction in log-logistic distribution based on type-II censored data. We assume that the scale and shape parameters have independent gamma priors. The Bayes estimators cannot be obtained in closed form; therefore, we use Metropolis Hasting algorithm to approximate the Bayes estimates of the unknown scale and shape parameters. We compare the performance of the Bayes estimator with the Maximum Likelihood estimators. In addition, we obtained the Bayesian credibility intervals and compared them with the Wald intervals. Moreover, we derived Bayesian point and interval predictors for future observation and investigated their performance using simulation techniques. And finally, we analysed real data set for illustration purposes. https://pubs.sciepub.com/ijbrm/6/1/1/ijbrm-6-1-1.pdf Bayes estimates Maximum Likelihood estimators Metropolis Hasting algorithm Predictive density Bayesian prediction Prediction interval