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dc.contributor.authorKuhlen, Nikolas
dc.date.accessioned2021-12-02T23:56:30Z
dc.date.available2021-12-02T23:56:30Z
dc.date.submitted2021-07-06
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331216
dc.description.abstractThis thesis consists of three essays that explore the use of probabilistic machine learning techniques in combination with information-theoretic concepts to answer economic questions. Over the past years, economists have started applying machine learning methods to a wide range of topics. Probabilistic methods in the context of unsupervised learning represent one particular modelling approach at the intersection of computer science and statistics. While widely used in applied statistics, these models, however, do not necessarily provide relevant and interpretable outputs from an economist's perspective. In this thesis, I appeal to information-theoretic methods to summarise the probabilistic information inferred from such models and construct economically meaningful measures.
dc.description.sponsorshipNikolas Kuhlen gratefully acknowledges the financial support of The Alan Turing Institute under research award No. TU/C/000030.
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectMachine Learning
dc.subjectText Analysis
dc.subjectNews
dc.subjectInformation Theory
dc.subjectInnovation
dc.subjectTechnology
dc.subjectPatents
dc.titleEssays on Probabilistic Machine Learning for Economics
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.identifier.doi10.17863/CAM.78661
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved/
rioxxterms.typeThesis
dc.type.qualificationtitlePhD in Economics
cam.supervisorCarvalho, Vasco


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