We need to optimize something (product, drug, prototype etc) by testing it on users.
Let's say we have a 6 factor 3 level full factorial experimental design. So we have 3x3x3x3x3x3 = 729 variations.
We want to limit a sample size of subjects. So we assume to use Multi-Armed Bandit algorithm (MAB), particularly Thompson sampling.
In addition, I think machine learning algorithms such as random forests can help.
Any thoughts on how to implement that from methodological point of view?
Tags: Design, Machine, experiments, learning, of
Posted 1 March 2021
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