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Currently test to recover parameters in the unscaled variance model fails.
I found found that increasing the sample size allows the parameters to be recovered, but this is very slow to fit.
Warning: 4000 of 4000 (100.0%) transitions hit the maximum treedepth limit of 10.
ess_bulk test fails for mu_c
The found parameters are not completely bad.
I am thinking whether posterior predictive checks could be used to determine if the mean parameters from the "bad" fit are actually quite a good fit for the data.
The text was updated successfully, but these errors were encountered:
I'm assuming that this implies the model works fine in the scaled case.
If memory serves me right ess_bulk failing means that they are very few independent samples and although the point estimates may be fine it means the overall coverage / shape of the posterior is likely not to be well described. Also means any variance estimates are likely to be inaccurate. I'm sure I read in one of the Stan articles that ESS > 100 is a minimum standard.
Did you look into the code at all ? Wondering if I made an implementation mistake ? If not I am wondering if maybe this model just isn't recoverable and we should instead just limit it to the scaled version only ?
Currently test to recover parameters in the unscaled variance model fails.
Warning: 4000 of 4000 (100.0%) transitions hit the maximum treedepth limit of 10.
mu_c
The text was updated successfully, but these errors were encountered: