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With most recent ArviZ and PyMC3, the log-likelihoods are automatically computed when return_inferencedata=True.
I just wondered why the notebook cell ran the sampling for 2 hours and then took another hour or so to return.. Turns out I forgot to pass idata_kwargs=dict(log_likelihood=False), causing from_pymc3 to re-evaluate my model on all posterior samples, which for an ODE model is not that fast.
Thoughts on implementation
For long-running log-likelihood evaluations it would be nice to show a progress bar. It would help the user to diagnose what's taking so long.
There may be alternatives to obtain log-likelihoods from sampler stats?
The text was updated successfully, but these errors were encountered:
With most recent ArviZ and PyMC3, the log-likelihoods are automatically computed when
return_inferencedata=True
.I just wondered why the notebook cell ran the sampling for 2 hours and then took another hour or so to return.. Turns out I forgot to pass
idata_kwargs=dict(log_likelihood=False)
, causingfrom_pymc3
to re-evaluate my model on all posterior samples, which for an ODE model is not that fast.Thoughts on implementation
For long-running log-likelihood evaluations it would be nice to show a progress bar. It would help the user to diagnose what's taking so long.
There may be alternatives to obtain log-likelihoods from sampler stats?
The text was updated successfully, but these errors were encountered: