LPPD stands for log point-wise predictive density
- inference is based on each data point
WAIC
- makes no assumption about the shape of the posterior (aic makes normality assumption on the posterior distribution)
- Approximate on out of sample deviance
- Converge to loocv
- Log posterior predictive density + penalty proportional to the variance in the posterior predictions (larger number of parameters -> bigger variance)
- guess the out-of-sample K-L Divergence
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