Wednesday, September 6, 2017 - 10:40am - 11:15am
Erkki Somersalo (Case Western Reserve University)
When observed data are used to infer on parameters that are not directly observable, usually an inverse problem needs to be solved. Characteristic for inverse problems is their ill-posedness, which in practice means that small errors in data may propagate to huge inconsistencies in the solution if the problem is not properly regularized or augmented with prior information. An important part of solving inverse problems is to carefully model the noise that in addition to the exogenous noise contains possible modeling errors.
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