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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