Meta-Uncertainty represents a fully probabilistic framework for quantifying the uncertainty over Bayesian posterior model probabilities (PMPs) using meta-models. Meta-models integrate simulated and observed data into a predictive distribution for new PMPs and help reduce overconfidence and estimate the PMPs in future replication studies.
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May 25, 2023
Schmitt, Marvin, 2023, "Replication Code for: Meta-Uncertainty in Bayesian Model Comparison",, DaRUS, V1, UNF:6:zUDr3KGdcaDCy+jFtcz8lA== [fileUNF]
This dataverse contains the code for the paper Meta-Uncertainty in Bayesian Model Comparison: Note that the R code is structured as a package, thus requiring a local installation with subsequent loading via library(MetaUncertaintyPaper)....
R Syntax - 26.5 KB - MD5: 406e38f92fbf258e7b0dff76e476c307
Unknown - 508 B - MD5: efd4078885864b7ee481d7a750a7fc92
R Syntax - 2.0 KB - MD5: f118a5dd0ac9405176e3e031b22cfdba
Jupyter Notebook - 268.9 KB - MD5: 51ef505d165efcfb6df367a909152beb
Tabular Data - 63.7 KB - 5 Variables, 1000 Observations - UNF:6:0S3zNKpiYSHYquMApi4R5A==
Plain Text - 0 B - MD5: d41d8cd98f00b204e9800998ecf8427e
Plain Text - 0 B - MD5: d41d8cd98f00b204e9800998ecf8427e
R Syntax - 649 B - MD5: 2efe46400894a3de05a31aa004a4484a
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