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We are currently treating p-outlier in a special way, and it implies that this parameter can be estimated, but only globally not via a regression. I don't think there is anything that absolutely forces us to do that, it is rather a legacy decision that wasn't revisited since a long time.
Instead, we should allow p-outlier to be included in the config dict, and if included, it should be treated like any other parameter.
In addition to a bit of extra logic on identifying the presence of p-outlier in the include argument, this will likely need a bit of surgery on our likelihood constructors to respect entry of p-outlier as a vector parameter.
The text was updated successfully, but these errors were encountered:
We are currently treating p-outlier in a special way, and it implies that this parameter can be estimated, but only globally not via a regression. I don't think there is anything that absolutely forces us to do that, it is rather a legacy decision that wasn't revisited since a long time.
Instead, we should allow p-outlier to be included in the config dict, and if included, it should be treated like any other parameter.
In addition to a bit of extra logic on identifying the presence of
p-outlier
in theinclude
argument, this will likely need a bit of surgery on our likelihood constructors to respect entry ofp-outlier
as a vector parameter.The text was updated successfully, but these errors were encountered: