@avehtari’s paper states in its abstract “… loo have various advantages over… DIC” Alas, after reading the paper I don’t think I can enumerate those advantages. Could someone illuminate me or point me to the right reading?
Thanks!
@avehtari’s paper states in its abstract “… loo have various advantages over… DIC” Alas, after reading the paper I don’t think I can enumerate those advantages. Could someone illuminate me or point me to the right reading?
Thanks!
From the paper
For example, DIC can produce negative estimates of the effective number of parameters in a model and it is not defined for singular models.
Unlike DIC, WAIC [and LOOIC] is invariant to parametrization
For DIC, there is a similar variance-based computation of the number of parameters that is
notoriously unreliable, but the WAIC [and LOOIC] version is more stable because it computes the variance separately for each data point and then takes the sum; the summing yields stability.
Moreover, DIC is not that Bayesian since you plug-in posterior means instead of integrating over a posterior distribution. And it is easy to tell from the LOOIC output when its assumptions do not hold, which is not the case for any other information criterion.
Ben was faster, but here’s my enumeration (and sorry for not making it more clear in the paper)
Thanks @avehtari and @bgoodri ! One more question. Any idea for when a vignette or similar for doing k-fold with loo 2.0 will be available?
Not soon, but maybe these two
and
help?