Limit standards are probability interval requirements for proportions. Simulation literature has focused on finding the confidence interval of the population proportion, which is inappropriate for limit standards. Further, some frequentist approaches cannot be utilized for highly reliable models, or models which produce no or few non-conforming trials. Bayesian methods provide approaches that can be utilized for all limit standard models. We consider a methodology developed for Bayesian reliability analysis, where historical data is used to define the a priori distribution of proportions p, and the customer desired a posteriori maximum probability is utilized to determine sample size for a replication.