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Broadly speaking, there are two views on Bayesian probability that interpret the probability concept in different ways.

According to the objectivist view, the rules of Bayesian statistics can be justified by requirements of rationality and consistency and interpreted as an extension of logic.

According to the subjectivist view, probability quantifies a " personal belief ".

Many modern machine learning methods are based on objectivist Bayesian principles.

In the Bayesian view, a probability is assigned to a hypothesis, whereas under the frequentist view, a hypothesis is typically tested without being assigned a probability.