Tractable computation of Bayes factors for robust model selection in the physical sciences

The Bayes factor is the ratio of marginal likelihood of two competing statistical models represented by their evidence, and can be used to precisely quantify the support for one model over another. Despite being conceptually simple and widely regarded as being a useful measure for model comparison, the Bayes factor has seen scarce use in the physical sciences due to the high cost of computing it. In this talk I will give an overview of some recent algorithmic and computational developments towards the tractable computational of the Bayes factor motivated by an example in the construction of conceptual models in hydrology.