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Bayesian inference over model-spaces increases the accuracy of model comparison and allows formal testing of hypotheses about model distributions in experimental populations
[article]
2019
arXiv
pre-print
Determining the best model or models for a particular data set, a process known as Bayesian model comparison, is a critical part of probabilistic inference. Typically, this process assumes a fixed model-space (that is, a fixed set of candidate models). However, it is also possible to perform Bayesian inference over model-spaces themselves, thus determining which spaces provide the best explanation for observed data. Model-space inference (MSI) allows the effective exclusion of poorly performing
arXiv:1901.01916v1
fatcat:xclunza2xbftzdmbx7sjogvbtu