Hi,

It is possible to use any log likelihood array to compute `loo`

, it doesnâ€™t need to be automatically retrieved when converting to InferenceData. There are some cases where getting pointwise log likelihood values automatically is not possible, there are other cases where getting the pointwise values automatically is possible but then itâ€™s not clear how to compute loo from there.

Luckily, `az.loo`

takes the data from the `log_likelihood`

group of the input inferencedata, and uses this to estimate `elpd_loo`

. You should therefore be able to add a deterministic (which will be stored in the posterior) and then â€śmoveâ€ť it to the `log_likelihood`

group. Another option is to compute that by hand afterwards with numpy and xarray (not too different from computing by hand in a deterministic). There is an example on hand generated log likelihood data in Refitting PyMC3 models with ArviZ (and xarray) â€” ArviZ dev documentation

I would like to note however, that defining and/or choosing what are your pointwise log likelihood values may not be obvious, there could even be multiple correct ways of doing so, the only difference between them being the question they answer. Take a look at this notebook for example