anesthetic

To generate publication-quality corner plots from nested sampling chains, you can load posterior samples from PolyChord, MultiNest, or UltraNest and plot 1D/2D marginals using KDE or histograms via the anesthetic library.

Yes, you can visualize posterior distributions from Cobaya or GetDist files. The Skill loads these chain formats directly to create marginal distributions and posterior-prior comparisons for model evaluation.

The best way to compute Bayesian statistics like logZ and D_KL from MCMC outputs involves loading posterior samples with the anesthetic library, which calculates logZ, D_KL, logL_P, and d_G for quantitative model evaluation.

Yes, this plotting method supports comparing prior vs posterior distributions. You can overlay prior and posterior samples on the same axes to visualize parameter constraints and update factors.

To transform and label parameters when plotting Bayesian posteriors, you manipulate loaded chain samples to derive new quantities, apply custom labels to axes, and manage multiple chains for comparative distribution analyses.

When creating 1D marginals and 2D corner plots for large parameter spaces, rendering scatter representations and KDEs can become computationally intensive, requiring careful axis management and parameter selection via the anesthetic APIs.