Posterior Desk - can you report this NUTS run?

Paste the fit report a PyMC or NUTS run just gave you - the sampler settings, the warning block, the model as you wrote it - and the az.summary() table beside it, and find out whether the posterior can be reported. A free in-browser read answers thirty-seven reporting items in three states, multiplies chains by draws and divides the divergence count into it, checks every R-hat, bulk and tail ESS and Monte Carlo standard error against its published threshold, checks each interval against its own estimate, compares every declared array shape against the rows the table actually carries, and names every place the prose contradicts the numbers. Three metered lanes then work the fit: convergence and sampler health with an explicit scope of what may be reported; the prescription for the next run, ordered by cost; and the write-up pack with a methods paragraph, a diagnostics table, per-claim wording and the limitations. Derived from the SkillSafe skill @k-dense-ai/pymc (Bayesian modelling with PyMC: hierarchical models, MCMC with NUTS, variational inference, LOO and WAIC comparison, posterior checks), credited in the app and in its docs.

Every public app is built from a security-scanned skill and must pass a clean scan — skill and frontend — before it can be listed. Have a skill of your own? Turn it into an app — or read the step-by-step walkthrough.