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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 performingarXiv:1901.01916v1 fatcat:xclunza2xbftzdmbx7sjogvbtu