Closed pkroenert closed 4 years ago
Hi!
So it seems you want to compute the elementwise log-likelihoods? In that case probably you should use elementwise_loglikelihoods
which @torfjelde just added to DynamicPPL. He also added an example to the ArviZ docs that shows how this function can be used to compute LOO.
Thank you very much @devmotion for this fast response and for the links! I will check these out and leave a reply once I see if this works.
Following the above example you can also just use ArviZ.waic
directly instead of implementing your own, if you'd like :+1:
Also, @devmotion I believe I misremembered when I said I thought "elementwise likelihood` was being used in the literature; I was thinking of "pointwise likelihood" :confused:
Also, @devmotion I believe I misremembered when I said I thought "elementwise likelihood` was being used in the literature; I was thinking of "pointwise likelihood" confused
We can just rename it to pointwise_loglikelihoods
and deprecate elementwise_loglikelihoods
:slightly_smiling_face:
We can just rename it to pointwise_loglikelihoods and deprecate elementwise_loglikelihoods
Honestly, that sounds like a good idea to me :+1:
This can be closed, right?
Yes, thanks again!
Hi there, I would like to compute the WAIC information criterion for a fitted Bayesian model.
However, in doing so one has to evaluate the computed log pointwise predictive density, as defined in Eq.(3) in: https://arxiv.org/pdf/1507.04544.pdf
Specifically I don't know how to compute the term:
which should be the likelihood with the sampled parameter values from the posterior.
How do I compute that with the returned chains from a NUTS() sampling?
Thanks for your help!!!