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Learning the fundamentals in a stationary environment. (English) Zbl 1390.91066

Summary: A Bayesian agent relies on past observations to learn the structure of a stationary process. We show that the agent’s predictions about near-horizon events become arbitrarily close to those he would have made if he knew the long-run empirical frequencies of the process.

MSC:

91A26 Rationality and learning in game theory
62C10 Bayesian problems; characterization of Bayes procedures
60G10 Stationary stochastic processes
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