Bayesian Epistemology
Beliefs can be updated by combining prior expectations with how strongly new evidence supports them.
The Mechanism: Bayesian reasoning updates a prior probability in light of evidence, producing a posterior probability. The change depends on both what was considered plausible beforehand and how likely the evidence would be under competing explanations.
The Philosophical Pivot: Evidence does not arrive in a vacuum. Prior beliefs shape interpretation, but they can also be revised when new observations provide strong reasons to do so.
The Horizon: Bayesian methods are used in statistics, science, and decision theory. Choosing priors can be contentious, and different prior assumptions may yield different conclusions when evidence is limited.
A numerical probability is not automatically objective simply because it is expressed with precision. The model, data, and assumptions all require scrutiny.
Bayesian updating offers a disciplined way to learn without demanding certainty, but it does not eliminate the need to decide which evidence is trustworthy.
Intellectual humility is not refusing to believe; it is being willing to let belief move in proportion to what the evidence can support.
A belief can be revised without being discarded, and confidence can change before conviction does.
What evidence would change my mind, and have I chosen that standard before seeing which side it favors?