Thompson Sampling

Thompson Sampling is a strategy used to make decisions that balance exploring new options and exploiting known good choices. Imagine having several machines (options) to play and wanting to find the best one. Thompson Sampling assigns a probability to each machine being the best based on past results, then randomly selects a machine according to these probabilities. This approach naturally balances trying new options (exploration) and sticking with successful ones (exploitation), often leading to better long-term results than simpler methods. It's widely used in online recommendations, advertising, and adaptive decision-making contexts.