diagnostics#
A collection of plotting utilities and metrics for evaluating trained Workflow
s.
Functions
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Computes the distance between observed and reference samples and generates a distribution of null sample distances by bootstrapping for hypothesis testing. |
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Creates the empirical CDFs for each marginal rank distribution and plots it against a uniform ECDF. |
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Creates the empirical CDFs for each marginal rank distribution and plots it against a uniform ECDF. |
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Computes an aggregate score for the marginal calibration error over an ensemble of approximate posteriors. |
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Creates and plots publication-ready histograms of rank statistics for simulation-based calibration (SBC) checks according to [1]. |
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A generic helper function to plot the losses of a series of training epochs and runs. |
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Plots the calibration curves, the ECEs and the marginal histograms of predicted posterior model probabilities for a model comparison problem. |
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Plots a confusion matrix for validating a neural network trained for Bayesian model comparison. |
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Generates a bivariate pair plot given posterior draws and optional prior or prior draws. |
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A more flexible pair plot function for multiple distributions based upon collected samples. |
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Computes the posterior contraction (PC) from prior to posterior for the given samples. |
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Creates and plots publication-ready recovery plot with true estimate vs. |
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Creates and plots publication-ready recovery plot of estimates vs. |
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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. |
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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 |