Thompson Sampling

Thompson Sampling is a probabilistic algorithm used for decision-making in the context of multi-armed bandit problems. It addresses the challenge of balancing exploration (trying out different options to gather information) and exploitation (choosing the best-known option to maximize reward). The method involves assigning a probability distribution to each option (or "arm") based on prior successes and failures. During each round of decision-making, an option is randomly selected according to its probability distribution, which reflects both the estimated value of the option and the uncertainty around that estimate. This approach allows the algorithm to effectively sample from the arm with the highest expected reward while still exploring other options, ultimately leading to improved decision-making over time as more data is accumulated. Thompson Sampling is widely used in various applications, including online advertising, clinical trials, and recommendation systems, due to its efficiency and effectiveness in optimizing rewards in uncertain environments.