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Radev" ], "authors": [ {"first" : "Marvin", "last" : "Schmitt"}, {"first" : "Desi R", "last" : "Ivanova"}, {"first" : "Daniel", "last" : "Habermann"}, {"first" : "Ullrich", "last" : "Koethe"}, {"first" : "Paul-Christian", "last" : "Bürkner"}, {"first" : "Stefan T.", "last" : "Radev"} ], "volume": "235", "bibtexKey": "schmitt2023selfconsistency" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2d2cdd48217351197f7b0967009aaeaed/mschmitt", "tags" : [ "imported" ], "intraHash" : "d2cdd48217351197f7b0967009aaeaed", "interHash" : "08810dce2f2b86c0eabed805aa949e58", "label" : "Smoking is associated with increased eryptosis, suicidal erythrocyte death, in a large population-based cohort", "user" : "mschmitt", "description" : "", "date" : "2024-09-30 15:14:38", "changeDate" : "2024-09-30 15:14:38", "count" : 1, "pub-type": "article", "journal": "Nature Scientific Reports", "year": "2024", "url": "", "author": [ "Marvin Schmitt*","Franz Ewendt*","Alexander Kluttig","Rafael Mikolajczyk","F. Bernhard Kraus","Wim Wätjen","Paul-Christian Bürkner","Gabriele I. Stangl","Michael Föller" ], "authors": [ {"first" : "Marvin", "last" : "Schmitt*"}, {"first" : "Franz", "last" : "Ewendt*"}, {"first" : "Alexander", "last" : "Kluttig"}, {"first" : "Rafael", "last" : "Mikolajczyk"}, {"first" : "F. Bernhard", "last" : "Kraus"}, {"first" : "Wim", "last" : "Wätjen"}, {"first" : "Paul-Christian", "last" : "Bürkner"}, {"first" : "Gabriele I.", "last" : "Stangl"}, {"first" : "Michael", "last" : "Föller"} ], "volume": "14","number": "1", "bibtexKey": "schmitt2024smoking" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/250208e77b4197b9a139b28516b45c467/mschmitt", "tags" : [ "EXC2075","SimTech","exc2075","graduateSchool","graduateschool","peerReviewed","pn6","postPrint","prePrint" ], "intraHash" : "50208e77b4197b9a139b28516b45c467", "interHash" : "7efbe1d85ce6665f0065010c27e413a7", "label" : "Meta-Uncertainty in Bayesian Model Comparison", "user" : "mschmitt", "description" : "", "date" : "2023-04-20 13:53:02", "changeDate" : "2023-04-27 21:12:24", "count" : 2, "pub-type": "inproceedings", "booktitle": "Proceedings of The 26th International Conference on Artificial Intelligence and Statistics","series": "Proceedings of Machine Learning Research","publisher":"PMLR", "year": "2023", "url": "https://proceedings.mlr.press/v206/schmitt23a.html", "author": [ "Marvin Schmitt","Stefan T. Radev","Paul-Christian Bürkner" ], "authors": [ {"first" : "Marvin", "last" : "Schmitt"}, {"first" : "Stefan T.", "last" : "Radev"}, {"first" : "Paul-Christian", "last" : "Bürkner"} ], "volume": "206","pages": "11--29","abstract": "Bayesian model comparison (BMC) offers a principled probabilistic approach to study and rank competing models. In standard BMC, we construct a discrete probability distribution over the set of possible models, conditional on the observed data of interest. These posterior model probabilities (PMPs) are measures of uncertainty, but\u2014when derived from a finite number of observations\u2014are also uncertain themselves. In this paper, we conceptualize distinct levels of uncertainty which arise in BMC. We explore a fully probabilistic framework for quantifying meta-uncertainty, resulting in an applied method to enhance any BMC workflow. Drawing on both Bayesian and frequentist techniques, we represent the uncertainty over the uncertain PMPs via meta-models which combine simulated and observed data into a predictive distribution for PMPs on new data. We demonstrate the utility of the proposed method in the context of conjugate Bayesian regression, likelihood-based inference with Markov chain Monte Carlo, and simulation-based inference with neural networks.", "bibtexKey": "schmitt2023metauncertainty" } ] }