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In some cases models are specified ("compressed") with frequency weights to speed up the fitting, however, this doesn't work well with loo as the Pareto k's indicates that all observations are heavily influencing the posterior, which of course it true on the aggregated level, but may not be true in the disaggregated level. Some kind of adjustment (I suppose disaggregation of the log-likelihood is a part of it) would be needed for such a case.
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Hi Staffan,
If you have a weighted likelihood e.g. target + = frequency_weight[i]*
likelihood [i], then I think you can still treat it as a vanilia
exchangebable model--the only difference is to extract this weighted
product as the pointwise likelihood and feed into loo. If some
frequency_weight[i]≈0, then I would expect a small k hat[i] accordingly.
On Fri, Feb 19, 2021 at 9:45 AM Staffan Betnér ***@***.***> wrote:
In some cases models are specified ("compressed") with frequency weights
to speed up the fitting, however, this doesn't work well with loo as the
Pareto k's indicates that all observations are heavily influencing the
posterior, which of course it true on the aggregated level, but may not be
true in the disaggregated level. Some kind of adjustment (I suppose
disaggregation of the log-likelihood is a part of it) would be needed for
such a case.
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In some cases models are specified ("compressed") with frequency weights to speed up the fitting, however, this doesn't work well with loo as the Pareto k's indicates that all observations are heavily influencing the posterior, which of course it true on the aggregated level, but may not be true in the disaggregated level. Some kind of adjustment (I suppose disaggregation of the log-likelihood is a part of it) would be needed for such a case.
The text was updated successfully, but these errors were encountered: