| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 7353039 | Games and Economic Behavior | 2018 | 9 Pages |
Abstract
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.
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Authors
Nabil I. Al-Najjar, Eran Shmaya,