plots#

Visual diagnosticss for evaluating trained Workflows.

Functions

bayes_factor_recovery(...[, model_names, ...])

Scatter plot of true vs. predicted log Bayes factors for M-model comparison.

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_histogram(estimates, targets[, ...])

Creates and plots publication-ready histograms of rank statistics for simulation-based calibration (SBC) checks according to [1].

coverage(estimates, targets[, difference, ...])

Creates coverage plots showing empirical coverage of posterior credible intervals.

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.

pairwise_bayes_factors(...[, model_names, ...])

Mean pairwise log Bayes factor heatmap, stratified by true generating model.

plot_quantity(values, targets, *[, ...])

Plot a quantity as a function of a variable for each variable key.

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.

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].