diagnostics

A collection of plotting utilities and metrics for evaluating trained Workflows.

Functions

bootstrap_comparison(observed_samples, ...)	Computes the distance between observed and reference samples and generates a distribution of null sample distances by bootstrapping for hypothesis testing.
calibration_ecdf(estimates, targets[, ...])	Creates the empirical CDFs for each marginal rank distribution and plots it against a uniform ECDF.
calibration_ecdf_from_quantiles(estimates, ...)	Creates the empirical CDFs for each marginal rank distribution and plots it against a uniform ECDF.
calibration_error(estimates, ...)	Computes an aggregate score for the marginal calibration error over an ensemble of approximate posteriors.
calibration_histogram(estimates, targets[, ...])	Creates and plots publication-ready histograms of rank statistics for simulation-based calibration (SBC) checks according to [1].
calibration_log_gamma(estimates, targets[, ...])	Compute the log gamma discrepancy statistic to test posterior calibration, see [1] for additional information.
loss(history[, train_key, val_key, ...])	A generic helper function to plot the losses of a series of training epochs and runs.
mc_calibration(pred_models, true_models[, ...])	Plots the calibration curves, the ECEs and the marginal histograms of predicted posterior model probabilities for a model comparison problem.
mc_confusion_matrix(pred_models, true_models)	Plots a confusion matrix for validating a neural network trained for Bayesian model comparison.
mmd_hypothesis_test(mmd_null[, ...])
pairs_posterior(estimates[, targets, ...])	Generates a bivariate pair plot given posterior draws and optional prior or prior draws.
pairs_quantity(values, targets, *[, ...])	A pair plot function to plot quantities against their generating parameter values.
pairs_samples([samples, dataset_id, ...])	A more flexible pair plot function for multiple distributions based upon collected samples.
plot_quantity(values, targets, *[, ...])	Plot a quantity as a function of a variable for each variable key.
posterior_contraction(estimates, ...)	Computes the posterior contraction (PC) from prior to posterior for the given samples.
posterior_z_score(estimates, ...)	Computes the posterior z-score from prior to posterior for the given samples according to [1]:
recovery(estimates, ...)	Creates and plots publication-ready recovery plot with true estimate vs. point estimate + uncertainty.
recovery_from_estimates(estimates, ...)	Creates and plots publication-ready recovery plot of estimates vs. targets.
summary_space_comparison(observed_data, ...)	Computes the distance between observed and reference data in the summary space and generates a distribution of distance values under the null hypothesis to assess model misspecification.
z_score_contraction(estimates, targets[, ...])	Implements a graphical check for global model sensitivity by plotting the posterior z-score over the posterior contraction for each set of posterior samples in estimates according to [1].