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The focus of statistical models (including Bayesian models) is on inference and uncertainty (both for parameter values and for predictions), the focus of ML models (including DL models) is on prediction and it is rarely possible to obtain any quantification of uncertainty.



> rarely possible to obtain any quantification of uncertainty.

Can't this be estimated via bootstrapping?


It is very challenging for complex models to know what's the coverage (and also could be extremely computationally intensive).




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