BNQD ow: A Bayesian non-parametric quasi-experimental design library using GP ow

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dc.contributor.advisorHinne, M.
dc.contributor.authorStarke, M. P.
dc.date.issued2020-07-01
dc.description.abstractIn this thesis we investigate the performance of a re-implementation of BNQD: a library for Bayesian Non-parametric Quasi-experimental Designs. BNQD functions as a Bayesian framework for quasi-experiments, and is capable of inferring causality under di erent assumptions than the ones required by a randomised controlled trial. We will compare the performance of this new implementation to the previous one, as well as examine the e ectiveness of the features that were previously unavailable.en_US
dc.embargo.lift10000-01-01
dc.embargo.typePermanent embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/12743
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleBNQD ow: A Bayesian non-parametric quasi-experimental design library using GP owen_US
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