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Radical Probabilism [Transcript]

Are there any other detailed descriptions of what a "Jeffrey update" might look like or how one would perform one?

I think I get the point of there being "rationality constraints" that don't, by implication, strictly require Bayesian updates. But are Jeffrey updates the entire set of possible updates that are required?

Can anyone describe a concrete example contrasting a Bayesian update and a Jeffrey update for the same circumstances, e.g. prior beliefs and new information learned?

It kinda seems like Jeffrey updates are 'possibly rational updates' but they're only justified if one can perform them for no possible (or knowable) reason. That doesn't seem practical – how could that work?