Replying again as I missed out a vital piece. Bayesian reasoning is an online process so after every decision one has to update the priors. The next time one has to use the reason engine one should work with the most recent prior. An alternative but equivalent way of stating the same is that one should look at the entire past to form the valid prior of that instant.

Lets take the example. There is a one to one correspondence with fictitious counts and priors. One way of encoding a 50:50 prior is to construct a possibly fictitious but representative past of (say) 2000 samples split into 1000 guilty and a 1000 not-guilty. After each prediction and assuming that the truth gets known one has to update the counts appropriately, so that the next time we use a different prior.

Our initial prior may be wrong but it will approach the correct one asymptotically. But how fast it approaches the true prior depends on how wrong our initial prior was.