API Overview

Plots

Name	Description
`plot_autocorr`	Bar plot of the autocorrelation function for a sequence of data.
`plot_compare`	Summary plot for model comparison.
`plot_density`	Generate KDE plots for continuous variables and histograms for discrete ones.
`plot_dist`	Plot distribution as histogram or kernel density estimates.
`plot_elpd`	Plot a scatter or hexbin matrix of the sampled parameters.
`plot_energy`	Plot energy transition distribution and marginal energy distribution in HMC algorithms.
`plot_ess`	Plot quantile, local or evolution of effective sample sizes (ESS).
`plot_forest`	Forest plot to compare credible intervals from a number of distributions.
`plot_hpd`	Plot hpd intervals for regression data.
`plot_joint`	Plot a scatter or hexbin of two variables with their respective marginals distributions.
`plot_kde`	1D or 2D KDE plot taking into account boundary conditions.
`plot_khat`	Plot Pareto tail indices.
`plot_loo_pit`	Plot Leave-One-Out (LOO) probability integral transformation (PIT) predictive checks.
`plot_mcse`	Plot quantile, local or evolution of effective sample sizes (ESS).
`plot_pair`	Plot a scatter or hexbin matrix of the sampled parameters.
`plot_parallel`	Plot parallel coordinates plot showing posterior points with and without divergences.
`plot_posterior`	Plot Posterior densities in the style of John K.
`plot_ppc`	Plot for posterior predictive checks.
`plot_rank`	Plot rank order statistics of chains.
`plot_trace`	Plot distribution (histogram or kernel density estimates) and sampled values.
`plot_violin`	Plot posterior of traces as violin plot.

Stats

Name	Description
`summarystats`	Compute summary statistics on an `InferenceData`
`compare`	Compare models based on WAIC or LOO cross-validation.
`hpd`	Calculate highest posterior density (HPD) of array for given credible_interval.
`loo`	Pareto-smoothed importance sampling leave-one-out (LOO) cross-validation.
`loo_pit`	Compute leave-one-out probability integral transform (PIT) values.
`psislw`	Pareto smoothed importance sampling (PSIS).
`r2_score`	$R^2$ for Bayesian regression models.
`waic`	Calculate the widely available information criterion (WAIC).

Diagnostics

Name	Description
`bfmi`	Calculate the estimated Bayesian fraction of missing information (BFMI).
`geweke`	Compute $z$-scores for convergence diagnostics.
`ess`	Calculate estimate of the effective sample size (ESS).
`rhat`	Compute estimate of rank normalized split-$\hat{R}$ for a set of traces.
`mcse`	Calculate Markov Chain Standard Error statistic (MCSE).

Stats utils

Name	Description
`autocov`	Compute autocovariance estimates for every lag for the input array.
`autocorr`	Compute autocorrelation using FFT for every lag for the input array.
`make_ufunc`	Make ufunc from a function taking 1D array input.
`wrap_xarray_ufunc`	Wrap `make_ufunc` with `xarray.apply_ufunc`.

Data

Name	Description
`InferenceData`	Container for inference data storage using xarray.
`convert_to_inference_data`	Convert a supported object to an `InferenceData`.
`load_arviz_data`	Load a local or remote pre-made dataset.
`to_netcdf`	Save dataset as a netcdf file.
`from_netcdf`	Load netcdf file back into an `InferenceData`.
`from_namedtuple`	Convert `NamedTuple` data into an `InferenceData`.
`from_dict`	Convert `Dict` data into an `InferenceData`.
`from_cmdstan`	Convert `CmdStan` data into an `InferenceData`.
`from_mcmcchains`	Convert `MCMCChains` data into an `InferenceData`.
`concat`	Concatenate `InferenceData` objects.
`concat!`	Concatenate `InferenceData` objects in-place.

Utils

Name	Description
`with_interactive_backend`	Change plotting backend temporarily.

rcParams

Name	Description
`with_rc_context`	Change ArviZ's matplotlib-style rc settings temporarily.