arviz.plot_posterior(data, var_names=None, filter_vars=None, combine_dims=None, transform=None, coords=None, grid=None, figsize=None, textsize=None, hdi_prob=None, multimodal=False, skipna=False, round_to=None, point_estimate='auto', group='posterior', rope=None, ref_val=None, rope_color='C2', ref_val_color='C1', kind=None, bw='default', circular=False, bins=None, labeller=None, ax=None, backend=None, backend_kwargs=None, show=None, **kwargs)[source]#

Plot Posterior densities in the style of John K. Kruschke’s book.

data: obj

Any object that can be converted to an arviz.InferenceData object. Refer to the documentation of arviz.convert_to_dataset() for details

var_names: list of variable names

Variables to be plotted, two variables are required. Prefix the variables with ~ when you want to exclude them from the plot.

filter_vars: {None, “like”, “regex”}, optional, default=None

If None (default), interpret var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names. A la pandas.filter.

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.

transform: callable

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

coords: mapping, optional

Coordinates of var_names to be plotted. Passed to xarray.Dataset.sel()


Number of rows and columns. Defaults to None, the rows and columns are automatically inferred.

figsize: tuple

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.

hdi_prob: float, optional

Plots highest density interval for chosen percentage of density. Use ‘hide’ to hide the highest density interval. Defaults to 0.94.

multimodal: bool

If true (default) it may compute more than one credible interval if the distribution is multimodal and the modes are well separated.


If true ignores nan values when computing the hdi and point estimates. Defaults to false.

round_to: int, optional

Controls formatting of floats. Defaults to 2 or the integer part, whichever is bigger.

point_estimate: Optional[str]

Plot point estimate per variable. Values should be ‘mean’, ‘median’, ‘mode’ or None. Defaults to ‘auto’ i.e. it falls back to default set in rcParams.

group: str, optional

Specifies which InferenceData group should be plotted. Defaults to ‘posterior’.

ropelist, tuple or dictionary of {str: tuples or lists}, optional

A dictionary of tuples with the lower and upper values of the Region Of Practical Equivalence. See this section for usage examples.

ref_val: float or dictionary of floats

display the percentage below and above the values in ref_val. Must be None (default), a constant, a list or a dictionary like see an example below. If a list is provided, its length should match the number of variables.

rope_color: str, optional

Specifies the color of ROPE and displayed percentage within ROPE

ref_val_color: str, optional

Specifies the color of the displayed percentage

kind: str

Type of plot to display (kde or hist) For discrete variables this argument is ignored and a histogram is always used. Defaults to rcParam plot.density_kind

bw: float or str, optional

If numeric, indicates the bandwidth and must be positive. If str, indicates the method to estimate the bandwidth and must be one of “scott”, “silverman”, “isj” or “experimental” when circular is False and “taylor” (for now) when circular is True. Defaults to “default” which means “experimental” when variable is not circular and “taylor” when it is. Only works if kind == kde.

circular: bool, optional

If True, it interprets the values passed are from a circular variable measured in radians and a circular KDE is used. Only valid for 1D KDE. Defaults to False. Only works if kind == kde.

bins: integer or sequence or ‘auto’, optional

Controls the number of bins,accepts the same keywords matplotlib.pyplot.hist() does. Only works if kind == hist. If None (default) it will use auto for continuous variables and range(xmin, xmax + 1) for discrete variables.

labellerlabeller instance, optional

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

ax: numpy array-like of matplotlib axes or bokeh figures, optional

A 2D array of locations into which to plot the densities. If not supplied, Arviz will create its own array of plot areas (and return it).

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()

show: bool, optional

Call backend show function.


Passed as-is to matplotlib.pyplot.hist() or matplotlib.pyplot.plot() function depending on the value of kind.

axes: matplotlib axes or bokeh figures

See also


Plot distribution as histogram or kernel density estimates.


Generate KDE plots for continuous variables and histograms for discrete ones.


Forest plot to compare HDI intervals from a number of distributions.


Show a default kernel density plot following style of John Kruschke

>>> import arviz as az
>>> data = az.load_arviz_data('centered_eight')
>>> az.plot_posterior(data)

Plot subset variables by specifying variable name exactly

>>> az.plot_posterior(data, var_names=['mu'])

Plot Region of Practical Equivalence (rope) and select variables with regular expressions

>>> az.plot_posterior(data, var_names=['mu', '^the'], filter_vars="regex", rope=(-1, 1))

Plot Region of Practical Equivalence for selected distributions

>>> rope = {'mu': [{'rope': (-2, 2)}], 'theta': [{'school': 'Choate', 'rope': (2, 4)}]}
>>> az.plot_posterior(data, var_names=['mu', 'theta'], rope=rope)

Using coords argument to plot only a subset of data

>>> coords = {"school": ["Choate","Phillips Exeter"]}
>>> az.plot_posterior(data, var_names=["mu", "theta"], coords=coords)

Add reference lines

>>> az.plot_posterior(data, var_names=['mu', 'theta'], ref_val=0)

Show point estimate of distribution

>>> az.plot_posterior(data, var_names=['mu', 'theta'], point_estimate='mode')

Show reference values using variable names and coordinates

>>> az.plot_posterior(data, ref_val= {"theta": [{"school": "Deerfield", "ref_val": 4},
...                                             {"school": "Choate", "ref_val": 3}]})

Show reference values using a list

>>> az.plot_posterior(data, ref_val=[1] + [5] * 8 + [1])

Plot posterior as a histogram

>>> az.plot_posterior(data, var_names=['mu'], kind='hist')

Change size of highest density interval

>>> az.plot_posterior(data, var_names=['mu'], hdi_prob=.75)