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Summary Statistic Selection with Reinforcement Learning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. , p. 38
Series
UPTEC F, ISSN 1401-5757 ; 19048
Keywords [en]
Summary Statistics, Approximate Bayesian Computation, Reinforcement Learning, Machine Learning, Multi-Armed Bandit, Subset Selection, Minimizing Entropy, Approximate Sufficiency, Direct, Halving, SAR, OCBA-m, Racing
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-390838OAI: oai:DiVA.org:uu-390838DiVA, id: diva2:1342908
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2019-08-19 Created: 2019-08-14 Last updated: 2019-08-19Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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