Bayesian Modeling of Time Series Data (BayModTS)

DOI

BayModTS is a FAIR workflow for processing highly variable and sparse data. The code and results of the examples in the BayModTS paper are stored in this repository. A maintained version of BayModTS that can be applied to your personal applications can be found on Git Hub.

Identifier
DOI https://doi.org/10.18419/darus-3876
Related Identifier IsCitedBy https://doi.org/10.1093/bioinformatics/btae312
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-3876
Provenance
Creator Höpfl, Sebastian ORCID logo
Publisher DaRUS
Contributor Höpfl, Sebastian; Radde, Nicole
Publication Year 2024
Funding Reference DFG FOR 5151 - 436883643 ; DFG EXC 2075 - 390740016
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Höpfl, Sebastian (University of Stuttgart); Radde, Nicole (University of Stuttgart)
Representation
Resource Type Dataset
Format application/zip
Size 850053521; 833326785; 292585173; 239306911; 246690428; 656396758; 96612662
Version 1.1
Discipline Life Sciences; Medicine