Are you aware of the existing work on ignorance priors, for instance the maximum entropy prior (if I remember properly this is Jeffrey’s prior and gives rise to the KT estimator), also the improper prior which effectively places almost all of the weight on 0 and 1?
Interestingly, the universal distribution does not include continuous parameters but does end up dominating any computable rule for assigning probabilities, including these families of conjugate priors.
Are you aware of the existing work on ignorance priors, for instance the maximum entropy prior (if I remember properly this is Jeffrey’s prior and gives rise to the KT estimator), also the improper prior which effectively places almost all of the weight on 0 and 1? Interestingly, the universal distribution does not include continuous parameters but does end up dominating any computable rule for assigning probabilities, including these families of conjugate priors.