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Statistical model criticism

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If you have a question about this talk, please contact Konstantina Palla.

Statistical analyses rely upon assumptions. When these assumptions are not met we may draw erroneous inferences. Statistical model criticism / checking procedures attempt to test these assumptions and give insight into how a statistical model might be expanded to better capture aspects of the data. I will discuss classical diagnostics for linear regression, Bayesian model criticism procedures and some advanced methods.

This talk is part of the Machine Learning Reading Group @ CUED series.

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