The Speed prior is a statistical method used primarily in the context of Bayesian statistics for model selection, particularly when dealing with models that involve multiple parameters, such as in regression settings. It was introduced to help address issues related to the selection of models that may have different levels of complexity. The Speed prior acts as a prior distribution on the coefficients in a regression model, allowing for variable selection and shrinkage while promoting sparsity in the model.
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