Échantillonnage de Thompson

Anglais

Thompson sampling

In artificial intelligence, Thompson sampling,[1] named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists in choosing the action that maximizes the expected reward with respect to a randomly drawn belief.