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Cost-based feature selection for network model choice
This approach reduced the computational cost by a factor of 50 without affecting classification accuracy.
ABC random forests for Bayesian parameter inference
We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level.
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