The distinction here may be quite simple: an objective Bayesian accepts Bayes’ Theorem, a subjective one does not. After all, Bayes’ Theorem posits that repeated adjustments of priors based on new posterioris from the latest observations will asymptotically converge on the “true” probability distribution. That is only meaningful if one believes in an objective, “true” probability distribution (and of course assuming that certain necessary conditions hold regarding the underlying distribution and its dimensionality).
The distinction here may be quite simple: an objective Bayesian accepts Bayes’ Theorem, a subjective one does not. After all, Bayes’ Theorem posits that repeated adjustments of priors based on new posterioris from the latest observations will asymptotically converge on the “true” probability distribution. That is only meaningful if one believes in an objective, “true” probability distribution (and of course assuming that certain necessary conditions hold regarding the underlying distribution and its dimensionality).