Is it really worth doing Bayesian Analysis if you have no idea about Priors? I have heard that if you use uniform priors in Bayesian Analysis, it is the same as doing Frequentist Analysis. If you are creating statistical models and you really have no idea about the prior distribution of the parameters in your model (e.g. maybe you only know that the parameters have to be greater than 0 since they represent concepts in the real world, e.g. human height) - does it really make sense to use Bayesian based models?
I have heard of concepts such as Non-Informative Priors and Jeffery's Priors - but it still seems to me that you can not "pull something out of nothing". If you really have no idea about the values and distributions for your prior parameters - statistically speaking, it would not seem "responsible" to select anything but Uniform Priors for your parameters - thus making your analysis quite similar (if not identical) to the Frequentist approach.
Does anyone have any comments to add to this?
Thanks

Bayesian and frequentist analyses are not equivalent under non-informative priors in any general sense.  For one thing, Bayesian probabilities are exact and frequentist p-values and confidence intervals are approximate except under a very select choice of models.  For another thing, the "equivalence" breaks down if there is more than one look at the data, i.e., sequential analysis is being done.
I can't think of a situation where there is no information about a parameter.  Think about comparing two treatments for treating blood pressure.  If you use a flat prior for the treatment effect, such a prior allows the treatment effect to be 100,000mmHg.  We know at the very least that the treatment will not shift mean blood pressure by more than 50mmHg.  We know more than that in many situations, e.g., that the probability of more than a 10mmHg shift in mean blood pressure is less than 0.025 likely.
Some resources about Bayes may be found here.
