arviz.plot_dist_comparison(data, kind='latent', figsize=None, textsize=None, var_names=None, coords=None, combine_dims=None, transform=None, legend=True, labeller=None, ax=None, prior_kwargs=None, posterior_kwargs=None, observed_kwargs=None, backend=None, backend_kwargs=None, show=None)[source]#

Plot to compare fitted and unfitted distributions.

The resulting plots will show the compared distributions both on separate axes (particularly useful when one of them is substantially tighter than another), and plotted together, so three plots per distribution

dataInferenceData object

arviz.InferenceData object containing the posterior/prior data.


kind of plot to display {“latent”, “observed”}, defaults to ‘latent’. “latent” includes {“prior”, “posterior”} and “observed” includes {“observed_data”, “prior_predictive”, “posterior_predictive”}


Figure size. If None it will be defined automatically.

textsize: float

Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize.

var_namesstr, list, list of lists

if str, plot the variable. if list, plot all the variables in list of all groups. if list of lists, plot the vars of groups in respective lists.


Dictionary mapping dimensions to selected coordinates to be plotted. Dimensions without a mapping specified will include all coordinates for that dimension.

combine_dimsset_like of str, optional

List of dimensions to reduce. Defaults to reducing only the “chain” and “draw” dimensions. See the this section for usage examples.


Function to transform data (defaults to None i.e. the identity function)


Add legend to figure. By default True.

labellerlabeller instance, optional

Class providing the method make_pp_label to generate the labels in the plot. Read the Label guide for more details and usage examples.

ax: axes, optional

Matplotlib axes: The ax argument should have shape (nvars, 3), where the last column is for the combined before/after plots and columns 0 and 1 are for the before and after plots, respectively.

prior_kwargsdicts, optional

Additional keywords passed to arviz.plot_dist() for prior/predictive groups.

posterior_kwargsdicts, optional

Additional keywords passed to arviz.plot_dist() for posterior/predictive groups.

observed_kwargsdicts, optional

Additional keywords passed to arviz.plot_dist() for observed_data group.

backend: str, optional

Select plotting backend {“matplotlib”,”bokeh”}. Default “matplotlib”.

backend_kwargs: bool, optional

These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure(). For additional documentation check the plotting method of the backend.

showbool, optional

Call backend show function.

axesa numpy 2D array of matplotlib axes. Returned object will have shape (nvars, 3),
where the last column is the combined plot and the first columns are the single plots.

See also


Plot Bayesian p-value for observed data and Posterior/Prior predictive.


Plot the prior/posterior plot for specified vars and coords.

>>> import arviz as az
>>> data = az.load_arviz_data('rugby')
>>> az.plot_dist_comparison(data, var_names=["defs"], coords={"team" : ["Italy"]})