Percentile Intervals in Bayesian Inference are Overconfident

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Höpfl, Sebastian, 2024, "Percentile Intervals in Bayesian Inference are Overconfident", https://doi.org/10.18419/darus-4068, DaRUS, V1

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Description	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). 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.
Subject	Mathematical Sciences; Medicine, Health and Life Sciences
Keyword	Highest Density Interval (HDI), Highest Posterior Density (HPD), Bayesian Inference
Data Generation	Experiment
License/Data Use Agreement	Custom Dataset Terms

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	liver_resection_bayesian_inference.zip ZIP Archive - 208.8 MB Published Mar 19, 2024 5 Downloads MD5: 2a1c5c0fcc23335a335e7e3f3cfc37bf 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.	Preview "liver_resection_bayesian_inference.zip" Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation EndNote XML RIS BibTeX
	Plot_hpd_vd_hdi.py Python Source Code - 10.3 KB Published Mar 19, 2024 7 Downloads MD5: 228226319e0fb9ba66c4bdf9c0457b01 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	Access File File Access Public Download Options Python Source Code Download Metadata Data File Citation EndNote XML RIS BibTeX
	results - broader prior validation.zip ZIP Archive - 127.6 MB Published Mar 19, 2024 5 Downloads MD5: 3c835554ab5eef777d9c5d8c37abb1aa Validation of Bayesian Inference results using a broader normal prior	Preview "results - broader prior validation.zip" Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation EndNote XML RIS BibTeX

Citation Metadata

Persistent Identifier	doi:10.18419/darus-4068
Publication Date	2024-03-19
Title	Percentile Intervals in Bayesian Inference are Overconfident
Author	Höpfl, Sebastian (Universität Stuttgart) - ORCID: 0000-0002-5300-0915
Point of Contact	Use email button above to contact. Höpfl, Sebastian (Universität Stuttgart) Radde, Nicole (Universität Stuttgart)
Description	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). 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.
Subject	Mathematical Sciences; Medicine, Health and Life Sciences
Keyword	Highest Density Interval (HDI) Highest Posterior Density (HPD) Bayesian Inference https://www.wikidata.org/wiki/Q812535 (Wikidata)
Funding Information	DFG: FOR 5151 - 436883643 DFG: EXC 2075 - 390740016 BMBF: 031L0304B DFG: 465194077
Project	QuaLiPerF
Depositor	Höpfl, Sebastian
Deposit Date	2024-03-01

Process Metadata

Processing Methods

BayModTS (

The Bayesian Modelling of Time Series (BayModTS) workflow with just one experimental condition (= complete liver data) was used for Bayesian Inference. 10,000 samples were drawn with the Adaptive Metropolis Sampler.

The code can be found in GitHub: https://github.com/Systems-Theory-in-Systems-Biology/BayModTS)

Software

Python Version: 3.12.1 (https://www.python.org/downloads/release/python-3120/?ref=upstract.com)
pypesto Version: 0.4.1 (https://github.com/icb-dcm/pypesto) (Schälte, Y., Fröhlich, F., Stapor, P., Vanhoefer, J., Weindl, D., Lakrisenko, P., Raimúndez, E., Pathirana, D., Schmiester, L., Städter, P., Contento, L., Merkt, S., Dudkin, E., Grein, S., and Hasenauer, J. (2021). pyPESTO - Parameter EStimation TOolbox for python. doi: 10.5281/zenodo.5647404)
petab Version: 0.2.8 (https://github.com/PEtab-dev) (Schmiester, L., Schälte, Y., Bergmann, F. T., Camba, T., Dudkin, E., Egert, J., Fröhlich, F., Fuhrmann, L., Hauber, A. L., Kemmer, S., Lakrisenko, P., Loos, C., Merkt, S., Müller, W., Pathirana, D., Raimúndez, E., Refisch, L., Rosenblatt, M., Stapor, P. L., … Weindl, D. (2021). PEtab - Interoperable specification of parameter estimation problems in systems biology. PLOS Computational Biology, 17(1), 1-10. doi: 10.1371/journal.pcbi.1008646)
amici Version: 0.21.2 (https://github.com/AMICI-dev/amici) (Fröhlich, F., Weindl, D., Schälte, Y., Pathirana, D., Paszkowski, Ł., Lines, G.T., Stapor, P. and Hasenauer, J., 2021. AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics, btab227, doi: 10.1093/bioinformatics/btab227)
arviz Version: 0.17.0 (https://github.com/arviz-devs)

Engineering Metadata

Data Generation	Experiment
Measured Variables	Liver Volume

Software Metadata (CodeMeta 2.0)

Software Version	0.1
Development Status	Concept
Application Category	Analysis
Application Subcategory	Executable code
Programming Language	Python
Runtime Platform	Python 3.12
Operating Systems	Linux
Software Requirements	pypesto 0.4.1 https://pypesto.readthedocs.io/en/latest/index.html https://github.com/icb-dcm/pypesto amici 0.21.2 https://pypi.org/project/amici/ https://github.com/AMICI-dev/AMICI

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The research software in this dataset is licensed under MIT License.

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Our Community Norms as well as good scientific practices expect that proper credit is given via citation. Please use the data citation shown on the dataset page.

Custom terms specific to this dataset Custom Dataset Terms — the following Custom Dataset Terms have been defined for this dataset.

Terms of Use The research data in this dataset is licensed under CC BY 4.0.
The research software in this dataset is licensed under MIT License.

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