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1	Summary Statistic Selection with Reinforcement Learning Barkino, Iliam January 2019 (has links) Multi-armed bandit (MAB) algorithms could be used to select a subset of the k most informative summary statistics, from a pool of m possible summary statistics, by reformulating the subset selection problem as a MAB problem. This is suggested by experiments that tested five MAB algorithms (Direct, Halving, SAR, OCBA-m, and Racing) on the reformulated problem and comparing the results to two established subset selection algorithms (Minimizing Entropy and Approximate Sufficiency). The MAB algorithms yielded errors at par with the established methods, but in only a fraction of the time. Establishing MAB algorithms as a new standard for summary statistics subset selection could therefore save numerous scientists substantial amounts of time when selecting summary statistics for approximate bayesian computation. Summary Statistics Approximate Bayesian Computation Reinforcement Learning Machine Learning Multi-Armed Bandit Subset Selection Minimizing Entropy Approximate Sufficiency Direct Halving SAR OCBA-m Racing Other Computer and Information Science Annan data- och informationsvetenskap