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Applying Thompson Sampling to Online Hypothesis Testing

Show simple item record 2021-02-24T06:57:17Z 2021-02-24T06:57:17Z 2021-02-24
dc.title Applying Thompson Sampling to Online Hypothesis Testing en
ethesis.discipline none und
ethesis.department none und
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty.URI Helsingin yliopisto fi University of Helsinki en Helsingfors universitet sv
dct.creator Suominen, Henri
dct.issued 2021
dct.language.ISO639-2 eng
dct.abstract Online hypothesis testing occurs in many branches of science. Most notably it is of use when there are too many hypotheses to test with traditional multiple hypothesis testing or when the hypotheses are created one-by-one. When testing multiple hypotheses one-by-one, the order in which the hypotheses are tested often has great influence to the power of the procedure. In this thesis we investigate the applicability of reinforcement learning tools to solve the exploration – exploitation problem that often arises in online hypothesis testing. We show that a common reinforcement learning tool, Thompson sampling, can be used to gain a modest amount of power using a method for online hypothesis testing called alpha-investing. Finally we examine the size of this effect using both synthetic data and a practical case involving simulated data studying urban pollution. We found that, by choosing the order of tested hypothesis with Thompson sampling, the power of alpha investing is improved. The level of improvement depends on the assumptions that the experimenter is willing to make and their validity. In a practical situation the presented procedure rejected up to 6.8 percentage points more hypotheses than testing the hypotheses in a random order. en
dct.subject Multiple Hypothesis Testing
dct.subject Online Hypothesis Testing
dct.subject Reinforcement Learning
dct.language en
ethesis.isPublicationLicenseAccepted true
ethesis.language englanti fi
ethesis.language English en
ethesis.language engelska sv
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
ethesis.thesistype pro gradu-avhandlingar sv
dct.identifier.ethesis E-thesisID:a752647a-fce8-4b41-82d2-abc67c3369d9
dct.identifier.urn URN:NBN:fi:hulib-202102241532
dc.type.dcmitype Text
ethesis.facultystudyline Sovellettu matematiikka fi
ethesis.facultystudyline Applied Mathematics en
ethesis.facultystudyline Tillämpad matematik sv
ethesis.mastersdegreeprogram Matematiikan ja tilastotieteen maisteriohjelma fi
ethesis.mastersdegreeprogram Master's Programme in Mathematics and Statistics en
ethesis.mastersdegreeprogram Magisterprogrammet i matematik och statistik sv

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