Machine Learning Based in Quantum Mechanics and Theorem of Bayes

This paper proposes a hybrid model of machine learning, based at the principles of quantum mechanics in conjunction to Bayes theorem. With such construction, it is tested onto a scenario dictated by the Mitchell criteria. By assuming stochastic system whose main dependence is through time, the proposed model exhibits a high experience probability just at the beginning of processes. The results of this paper would corroborate the hypothesis that stochastic system might be strongly dependent at the very beginning or initial conditions.