API Reference


plot_autocorr(data[, var_names, max_lag, …]) Bar plot of the autocorrelation function for a sequence of data.
plot_compare(comp_df[, insample_dev, …]) Summary plot for model comparison.
plot_density(data[, group, data_labels, …]) Generate KDE plots for continuous variables and histograms for discrete ones.
plot_energy(data[, kind, bfmi, figsize, …]) Plot energy transition distribution and marginal energy distribution in HMC algorithms.
plot_forest(data[, kind, model_names, …]) Forest plot to compare credible intervals from a number of distributions.
plot_kde(values[, values2, cumulative, rug, …]) 1D or 2D KDE plot taking into account boundary conditions.
plot_parallel(data[, var_names, coords, …]) Plot parallel coordinates plot showing posterior points with and without divergences.
plot_posterior(data[, var_names, coords, …]) Plot Posterior densities in the style of John K.
plot_trace(data[, var_names, coords, …]) Plot samples histograms and values.
plot_pair(data[, var_names, coords, …]) Plot a scatter or hexbin matrix of the sampled parameters.
plot_joint(data[, var_names, coords, …]) Plot a scatter or hexbin of two variables with their respective marginals distributions.
plot_khat(khats[, figsize, textsize, …]) Plot Pareto tail indices.
plot_ppc(data[, kind, alpha, mean, figsize, …]) Plot for Posterior Predictive checks.


bfmi(energy) Calculate the estimated Bayesian fraction of missing information (BFMI).
compare(dataset_dict[, ic, method, …]) Compare models based on WAIC or LOO cross validation.
hpd(x[, credible_interval, circular]) Calculate highest posterior density (HPD) of array for given credible_interval.
loo(data[, pointwise, reff]) Pareto-smoothed importance sampling leave-one-out cross-validation.
r2_score(y_true, y_pred) R² for Bayesian regression models.
summary(data[, var_names, fmt, round_to, …]) Create a data frame with summary statistics.
waic(data[, pointwise]) Calculate the widely available information criterion.
psislw(log_weights[, reff]) Pareto smoothed importance sampling (PSIS).


effective_sample_size(data, *[, var_names]) Calculate estimate of the effective sample size.
rhat(data[, var_names]) Compute estimate of Split R-hat for a set of traces.
geweke(values[, first, last, intervals]) Compute z-scores for convergence diagnostics.


convert_to_inference_data(obj, *[, group, …]) Convert a supported object to an InferenceData object.
load_arviz_data([dataset, data_home]) Load a local or remote pre-made dataset.
to_netcdf(data, filename, *[, group, …]) Save dataset as a netcdf file.
from_netcdf(filename) Load netcdf file back into an arviz.InferenceData.
from_cmdstan([posterior, …]) Convert CmdStan data into an InferenceData object.
from_dict([posterior, posterior_predictive, …]) Convert Dictionary data into an InferenceData object.
from_emcee([sampler, var_names, arg_names, …]) Convert emcee data into an InferenceData object.
from_pymc3([trace, prior, …]) Convert pymc3 data into an InferenceData object.
from_pyro([posterior, coords, dims]) Convert pyro data into an InferenceData object.
from_pystan([posterior, …]) Convert PyStan data into an InferenceData object.