Concept
Concept
Thompson Sampling is a Bayesian approach to the multi-armed bandit problem that balances exploration and exploitation by sampling from the posterior distribution of each action's expected reward. It is particularly effective in online decision-making scenarios where uncertainty about action outcomes needs to be reduced over time through adaptive learning.
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