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Part 1: Document Description
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Citation |
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Title: |
Percentile Intervals in Bayesian Inference are Overconfident |
Identification Number: |
doi:10.18419/darus-4068 |
Distributor: |
DaRUS |
Date of Distribution: |
2024-03-19 |
Version: |
1 |
Bibliographic Citation: |
Höpfl, Sebastian, 2024, "Percentile Intervals in Bayesian Inference are Overconfident", https://doi.org/10.18419/darus-4068, DaRUS, V1 |
Citation |
|
Title: |
Percentile Intervals in Bayesian Inference are Overconfident |
Identification Number: |
doi:10.18419/darus-4068 |
Authoring Entity: |
Höpfl, Sebastian (Universität Stuttgart) |
Grant Number: |
FOR 5151 - 436883643 |
Grant Number: |
EXC 2075 - 390740016 |
Grant Number: |
031L0304B |
Grant Number: |
465194077 |
Distributor: |
DaRUS |
Access Authority: |
Höpfl, Sebastian |
Access Authority: |
Radde, Nicole |
Depositor: |
Höpfl, Sebastian |
Date of Deposit: |
2024-03-01 |
Holdings Information: |
https://doi.org/10.18419/darus-4068 |
Study Scope |
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Keywords: |
Mathematical Sciences, Medicine, Health and Life Sciences, Highest Density Interval (HDI), Highest Posterior Density (HPD), Bayesian Inference |
Abstract: |
<p>This dataset demonstrates the difference in calculating percentile Intervals as approximation for Highest Density Intervals (HDI) vs. Highest Posterior Density (HPD). This is demonstrated with extended partial liver resection data (ZeLeR-study, ethical vote: 2018-1246-Material).</p> The data includes Computed Tomography (CT) liver volume measurements of patients before (POD 0) and after partial hepatectomy. Liver volume was normalized per patient to the preoperative liver volume. was used to screen the liver regeneration courses. The Fujifilm Synapse3D software was used to calculate volume estimates from CT images. The data is structured in a tabular separated value file of the PEtab format. |
Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Label: |
liver_resection_bayesian_inference.zip |
Text: |
Bayesian Inference Results of the clinical dataset using BayModTS. Results include the posterior, the PEtab problem for inference. Further the results of the HPDR vs. PI comparison are included. |
Notes: |
application/zip |
Label: |
Plot_hpd_vd_hdi.py |
Text: |
Executable Pyhton file for the HPD vs HDI analysis. The posterior of the clinical data is calculated with the BayModTS workflow: https://github.com/Systems-Theory-in-Systems-Biology/BayModTS |
Notes: |
text/x-python |
Label: |
results - broader prior validation.zip |
Text: |
Validation of Bayesian Inference results using a broader normal prior |
Notes: |
application/zip |