Bayesian Statistics
Thompson Sampling is a probabilistic algorithm used for making decisions in uncertain environments, specifically for balancing exploration and exploitation in sequential decision-making scenarios. It leverages Bayesian inference to update the probability estimates of each option's success as new data becomes available, making it particularly effective in applications such as A/B testing and adaptive learning in machine learning.
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