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So after looking at the problem I'm actually working on, I realize an adaptive/sequential design isn't really what I'm after.
What I really want is a fractional factorial model that takes a prior (and minimizes regret between information learned and cumulative score). It seems like the goal of multi-armed bandit is to do exactly that, but I only want to do it once, assuming a fixed prior which doesn't update over time.
Do you think your monte-carlo Bayesian experimental design is the best way to do this, or can I utilize some of the insights from Thompson sampling to make this process a bit less computationally expensive (which is important for my particular use case)?
I still don't understand what you're trying to do. If you're trying to maximize test scores by increasing them through picking textbooks and this is done many times, you want a multi-armed bandit to help you find what is the best textbook over the many students exposed to different combinations. If you are throwing out the information from each batch and assuming the interventions are totally different each time, then your decision is made before you do any learning and... (read more)