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In dealing with the problem of establishing control limits in short run production, Bayesian approach provides a effective way for are short run process control and are particularly attractive. In this paper, two quality control models for short run process are presented based on Bayesian analysis and the two models are compared. Models are focused on normally distributed data. The first way to establish model is through the posterior density of mean and variance of normally distributed data respectively and the second way is through the posterior predictive density. And the results deduced from two different ways are compared.