Visualization

Bayesian diagnostic plots are generated using ArviZ to visualize MCMC traces, rank plots, energy, and posterior distributions. You simply specify which diagnostic figures are most important for your interpretation, and the Skill produces and saves them to your project's figures directory.

Standardizing styling for posterior summary figures involves applying consistent visual guidelines during plot generation. The Skill enforces standard styling for ArviZ plots, ensuring your posterior beliefs, effects, and model comparisons are presented in a clear, publication-ready format.

Yes, ArviZ plots support model comparison visuals that summarize posterior effects and differences. The Skill generates these substantive figures after model fitting to help you communicate and interpret model comparisons effectively for your audience.

Yes, this approach handles both MCMC diagnostics and substantive result reporting. It produces ArviZ-based diagnostic visuals like trace and energy plots alongside substantive figures that summarize posterior beliefs, ensuring comprehensive coverage for interpretation and reporting.

Forest plots and posterior predictive checks (PPCs) are needed when you want to validate model fit and summarize posterior distributions visually. The Skill generates these specific ArviZ diagnostic figures after model fitting to support clear interpretation and audience-appropriate reporting.

Asking what aspects of the results are most important before plotting ensures the generated figures align with your interpretation goals. The Skill always requests this user input first, guaranteeing the final ArviZ visuals and diagnostics are audience-appropriate and relevant.