We can never validate whether a model is true. In practice, “all models are wrong” (Box, 1976). However, we can try to uncover where the model goes wrong. Model criticism helps justify the model as an approximation or point to good directions for revising the model. For background, see the criticism tutorial.

Edward explores model criticism using

- point evaluations, such as mean squared error or classification accuracy;
- posterior predictive checks, for making probabilistic assessments of the model fit using discrepancy functions.

`ed.criticisms.evaluate`

`ed.criticisms.ppc`

`ed.criticisms.ppc_density_plot`

`ed.criticisms.ppc_stat_hist_plot`

Box, G. E. (1976). Science and statistics. *Journal of the American Statistical Association*, *71*(356), 791–799.