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Probabilistic programming for experimental design

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Scientists run experiments to distinguish between competing hypotheses, but how do we select the best experiment to run? The answer is often non-trivial, as there are usually many possible experiments but limited time and resources. I describe a system for Bayesian optimal experiment design (OED) based on probabilistic programming languages (PPLs): given hypotheses encoded as PPL models and an explicit definition of the experiment space, OED automates the search for experiments with high expected information gain. Additionally, I describe “adaptive OED ”, a framework for active learning: by updating our prior beliefs on hypotheses with the observed response to an experiment, OED can suggest further experiments to continue to tease apart hypotheses. I apply this system to two domains in cognitive psychology—sequence prediction and causal knowledge—and demonstrate that adaptive OED performs better than standard OED and other naive experiment selection procedures.

This talk is part of the Churchill Scholars Overly Awesome Research Symposium (ChuSOARS) series.

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