Generally speaking, a good Bayesian analysis includes what's known as a "sensitivity analysis" which seeks to measure how sensitive the results are to the particular choice of prior. Additionally, if strong prior assumptions are not available, an "uninformative" prior is used. In such cases, the results tend to be pretty close to those from frequentist methods, except the frequentist methods lack the Bayesian probabilistic interpretation.