While technically true, another way to say the same thing is "Bayesian reasoning helps you draw reasonable conclusions if the number of samples is low, while converging on the objective solution as the number of samples grows". Which has an entirely different ring to it.

I don't think this is technically correct - as Bernstein-Von Mises guarantee under certain conditions that the posterior is independent of the prior after sufficient data is observed.

http://en.m.wikipedia.org/wiki/Bernstein%E2%80%93von_Mises_t...

Consider an ideal black spherical 777, hilarity ensues...

But seriously, the strength of Bayes is in coping with an initially craptastic prior by updating probabilities (the posteriors) as (potentially noisy) evidence comes in. Now based on that data as it arrives, build an evolving model for the location with confidence intervals and probabilities.

Now refine your search to emphasize locations with both emerging high probability and high variance and hope the noise in the data isn't so pathological as to make the process worthless.

It's worth a shot.

I'd like to disagree with that. If your priori are particularly bad then you should expect your estimates to change quickly based on new evidence.

But what's the alternative? The same experts making the same guesswork with the same information.