Use Beta Distribution and Thompson Sampling to Beat The Multi-armed Bandit at the Casinoby@ryan-yu
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Use Beta Distribution and Thompson Sampling to Beat The Multi-armed Bandit at the Casino

February 29th 2020
15 min
by @ryan-yu 1,795 reads
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Beta distribution is a family of continuous probability distributions defined on the interval [0, 1] parametrized by two positive shape parameters, denoted by α and β, that appear as exponents of the random variable and control the shape of the distribution. We use Beta distribution to model the simplest form of the multi-armed bandit problem, which is the binary outcome/reward. In the casino example, each machine will pay a reward of $1 when the outcome is success, and $0 when it is fail. Our goal is to identify the machine with the highest probability of success.

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