Overcoming model simplifications when quantifying predictive uncertainty

21 Mar 2017George M. MathewsJohn Vial

It is generally accepted that all models are wrong -- the difficulty is determining which are useful. Here, a useful model is considered as one that is capable of combining data and expert knowledge, through an inversion or calibration process, to adequately characterize the uncertainty in predictions of interest... (read more)

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