| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Vákár, M.I.L. | |
| dc.contributor.author | Geurtsen, Mathan | |
| dc.date.accessioned | 2023-01-26T01:00:59Z | |
| dc.date.available | 2023-01-26T01:00:59Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/43449 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | This thesis concerns the building of a Bayesian model for a geological temperature proxy
based on oxygen isotope fractionation in carbonates. The major improvement over original
regressions is a better quantification of uncertainty and the use of partial pooling. Multiple
models are compared for further improvements. | |
| dc.title | Probabilistic Programming for temperature reconstructions
in the geological past to improve our understanding of
greenhouse climates | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | linear regression, Bayesian statistics, oxygen isotope temperature proxy, partial pooling, probabilistic programming, δ18Oc, δ18Ow, variability. | |
| dc.subject.courseuu | Artificial Intelligence | |
| dc.thesis.id | 13240 | |
