Learning and Long-run Fundamentals in Stationary Environments

We prove that a Bayesian agent observing a stationary process learns to make predictions as if the fundamental, defined as the ergodic component or the long run empirical frequencies of the process, was known to him. We interpret the ergodic representation as a decomposition of a stationary belief into risk and uncertainty.