Editing Bayesian probability (section)

==Bayesian methodology==
Bayesian methods are characterized by concepts and procedures as follows:
* The use of [[random variable]]s, or more generally unknown quantities,<ref name="rbp"/> to model all sources of [[uncertainty]] in statistical models including uncertainty resulting from lack of information (see also [[Uncertainty quantification#Aleatoric and epistemic uncertainty|aleatoric and epistemic uncertainty]]).
* The need to determine the prior [[probability distribution]] taking into account the available (prior) information.
* The sequential use of [[Bayes' theorem]]: as more data become available, calculate the posterior distribution using Bayes' theorem; subsequently, the posterior distribution becomes the next prior.
* While for the frequentist, a [[null hypothesis|hypothesis]] is a [[Proposition#Treatment in logic|proposition]] (which must be [[principle of bivalence|either true or false]]) so that the frequentist probability of a hypothesis is either 0 or 1, in Bayesian statistics, the probability that can be assigned to a hypothesis can also be in a range from 0 to 1 if the truth value is uncertain.