Example gallery# Mixed Plots# Forest Plot with ESS using plot_forest Traceplot using plot_trace Rank Bars Diagnostic with KDE using plot_trace Traceplot with Circular Variables using plot_trace Traceplot rank_vlines using plot_trace Distributions# Density Plot using plot_density Dist Plot using plot_dist Dot Plot using plot_dot ECDF Plot using plot_ecdf Forest Plot using plot_forest Ridgeplot using plot_forest Joint Plot using plot_pair KDE Plot using plot_kde 2D KDE using plot_kde 2D KDE with HDI Contours using plot_kde KDE quantiles using plot_kde Hexbin PairPlot using plot_pair KDE Pair Plot using plot_pair KDE Pair Plot with HDI Contours using plot_pair Point Estimate Pairplot using plot_pair Posterior Plot using plot_posterior Posterior Plot (reducing school dimension) using plot_posterior Violin plot using plot_violin Distribution Comparison# Density Plot (Comparison) using plot_density Forest Plot Comparison using plot_forest Single-Sided Violin Plot using plot_violin Inference Diagnostics# Autocorrelation Plot using plot_autocorr Energy Plot using plot_energy ESS Evolution Plot using plot_ess ESS Local Plot using plot_ess ESS Quantile Plot using plot_ess Quantile Monte Carlo Standard Error Plot using plot_mcse Quantile MCSE Errobar Plot using plot_mcse Pair Plot using plot_pair Parallel Plot using plot_parallel Rank plot using plot_rank Regression or Time Series# Plot HDI using plot_hdi Regression Plot using plot_lm Model Comparison# Compare Plot using plot_compare ELPD Plot using plot_elpd Pareto Shape Plot using plot_khat Model Checking# Bayesian u-value Plot using plot_bpv Bayesian p-value with T statistic Plot using plot_bpv LOO-PIT ECDF Plot using plot_loo_pit LOO-PIT Overlay Plot using plot_loo_pit Posterior Predictive Check Plot using plot_ppc Posterior Predictive Check Cumulative Plot using plot_ppc Separation Plot using plot_separation Styles# Use Matplotlib Styles with arviz.style.use().
