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Gravitational Wave Data Analysis: Improving the Normalizing Flow Architecture in flowMC for Efficient Bayesian Inference in Parameter Estimation

dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorBroeck, C.F.F. Van den
dc.contributor.authorGuo, Hong
dc.date.accessioned2025-08-21T00:00:57Z
dc.date.available2025-08-21T00:00:57Z
dc.date.issued2025
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/49808
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectGravitational Wave Data Analysis: Improving the Normalizing Flow Architecture in flowMC for Efficient Bayesian Inference in Parameter Estimation
dc.titleGravitational Wave Data Analysis: Improving the Normalizing Flow Architecture in flowMC for Efficient Bayesian Inference in Parameter Estimation
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsgravitational wave; gravity; machine learning; Bayesian inference; MCMC; normalizing flow; flow-based model; flowMC; flowJAX; generative model; parameter estimation
dc.subject.courseuuApplied Data Science
dc.thesis.id52116


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