The technical term for the condition you're interested in is consistency. There is a theorem (Doob) regarding the consistency of Bayes estimates. [1]
In simple cases it means that under any non-silly prior your posterior will converge to the truth. E.g. mean of a normal distribution with a normal prior.
The complex cases include things like models that expand when given more data.
The technical term for the condition you're interested in is consistency. There is a theorem (Doob) regarding the consistency of Bayes estimates. [1]
In simple cases it means that under any non-silly prior your posterior will converge to the truth. E.g. mean of a normal distribution with a normal prior.
The complex cases include things like models that expand when given more data.
[1] http://www4.stat.ncsu.edu/~sghosal/papers/bhurev.pdf