Weibull distribution is considered as one of the most widely distribution applied in real life, Its similar to normal distribution in the way of applications, it's also considered as one of the distributions that can applied in many fields such as industrial engineering to represent replaced and manufacturing time ,weather forecasting, and other scientific uses in reliability studies and survival function in medical and communication engineering fields.
In this paper, The scale parameter has been estimated for weibull distribution using Bayesian method based on Jeffery prior information as a first method , then enhanced by improving Jeffery prior information and then used as a second method ,moreover another Bayesian method has been suggested based on Jeffery's method also, then a comparison between Bayesian methods with other methods (Maximum likelihood estimator, Moment ,least squares) has been made and then applied using supposed shape parameters, scale parameter , and constant c ,sample sizes (10,20,30,50,100) Finally the results showed the superiority of Maximum likelihood ,While the second best estimation method bounced between the first and second Bayes method and Moments method.
