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The axioms of probability

AI-generated Abstract

A simple set of physically meaningful axioms for probability theory that unifies classical and quantum probability is proposed. The analysis reveals that some fundamental concepts in probability are model-dependent, challenging traditional views. Notably, Bayes' definition of conditional probability is identified as crucial in distinguishing different probabilistic models, particularly highlighting differences between Kolmogorovian and non-Kolmogorovian frameworks. The paper also discusses the quantum Brownian motion and its relation to quantum stochastic processes, presenting a detailed mathematical framework that bridges quantum theory with probabilistic models.