pymc-labs/pymc-modeling

arviz-diagnostics

Diagnose existing Bayesian inference output and evaluate fitted predictions with modern ArviZ DataTree summaries, divergences, rank R-hat, ESS and Monte Carlo precision. Use for posterior predictive checks, calibration, LOO/ELPD, Pareto k, stacking, grouped…

لغة النص الأصلي: الإنجليزية

prior-elicitation

Elicit and check Bayesian prior assumptions in meaningful units. Use for expert quantiles, PreliZ, constrained priors, prior-predictive plausibility and prior sensitivity, including power scaling, regularized horseshoe and R2D2 assumptions.

لغة النص الأصلي: الإنجليزية

pymc-modeling

Build, revise, and debug Bayesian models with PyMC. Use when choosing likelihoods and priors, specifying model/data dimensions, fitting hierarchical models, GPs, time series, mixtures, BART or splines, selecting inference methods, resolving failed initial…

لغة النص الأصلي: الإنجليزية

pytensor-workflows

Build, differentiate, compile, and debug PyTensor symbolic computations for models. Use for symbolic tensor shape or dtype errors, named dimensions, indexing and broadcasting, vectorization and scan, numerical or sparse operations, symbolic RNG state, graph…

لغة النص الأصلي: الإنجليزية