A Bayesian approach for modeling ceramic's fracture strength

A Bayesian approach for modeling ceramic's fracture strength

In modeling fracture strengths, a researcher may have rough predictions about the characteristics of his trial. Besides, some prior knowledge could also be available from historical data. In this paper, this prior knowlEdge is used via Bayes rule to build more reliable Estimations of parameters of the Weibull distribution which is widely employed in reliability and lifetime studies. This approach is really helpful especially when dealing with Small sample sizes. After applying different priors, the sampling-importanceresampling technique, which is a special Markov Chain Monte Carlo method. is used to study the posterior properties. Some num simulations are given to Compare the statistical properties of Estimations by the Bayes and the common maximum likelihood and linear regression methods.