Bayesian Bandits — Thompson Sampling

For problems where you want to learn and exploit simultaneously, use multi-armed bandits with Thompson sampling. Unlike A/B testing (which fixes traffic allocation and decides at the end), bandits adapt allocation during the experiment — sending more traffic to better-performing arms and reducing the total number of users exposed to inferior variants.

When to use this skill