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http://hdl.handle.net/1942/38539
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DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | FAES, Christel | |
dc.contributor.advisor | DEVLEESSCHAUWER, Brecht | |
dc.contributor.author | Van Den Neucker, Sophie | |
dc.date.accessioned | 2022-09-26T08:21:14Z | - |
dc.date.available | 2022-09-26T08:21:14Z | - |
dc.date.issued | 2022 | |
dc.identifier.uri | http://hdl.handle.net/1942/38539 | - |
dc.format.mimetype | Application/pdf | |
dc.language | en | |
dc.publisher | tUL | |
dc.title | Spatiotemporal Gaussian process regression to estimate the global burden of disease | |
dc.type | Theses and Dissertations | |
local.bibliographicCitation.jcat | T2 | |
dc.description.notes | Master of Statistics and Data Science-Quantitative Epidemiology | |
local.type.specified | Master thesis | |
item.fullcitation | Van Den Neucker, Sophie (2022) Spatiotemporal Gaussian process regression to estimate the global burden of disease. | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
item.contributor | Van Den Neucker, Sophie | - |
Appears in Collections: | Master theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
38b417f4-b54c-4a24-851d-fde8b64fab38.pdf | 16.01 MB | Adobe PDF | View/Open |
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