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Thompson Sampling: Why Randomised Exploration Beats Greedy Optimisation

Imagine you run an online experiment with several versions of a webpage, several drug doses, or several ad creatives. Each option pays off at an unknown rate, and every trial costs you a chance to earn from the best option you already know. Play it safe and you may lock in a mediocre choice forever…

Read the full story at DEV Community — Machine Learning ↗

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  1. 2026-10-30 17:03 · DEV Community — Machine Learning
    Thompson Sampling: Why Randomised Exploration Beats Greedy Optimisation

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