Thompson

Thompson sampling is a method used in decision-making and online learning to select options that balance exploring new possibilities and exploiting known good choices. Imagine you’re trying different ads to see which one performs best; Thompson sampling uses probability to estimate each ad’s success rate based on past results. It then randomly chooses ads according to these probabilities, effectively exploring less certain options while still favoring strong performers. Over time, this approach helps identify the best options efficiently, making it popular in areas like website optimization, clinical trials, and adaptive algorithms where balancing discovery and certainty is essential.