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

I’ll approach the answer from practical point of view, not philosophy. You almost always have priors or can construct them.
In the industry when we approach the expert we help them formulate priors. we can’t simply ask a portfolio manager to supply us with prior distribution of loan defaults. That won’t go anywhere. Instead we approach them with surveys that allow us to formulate the priors. For instance we may start with beta distribution then ask a few questions to determine its shape. Beta distribution is bounded between 0 and 1 which suites this case.
