arviz_plots.plot_bf

arviz_plots.plot_bf(dt, var_names, ref_val=0, kind=None, sample_dims=None, plot_collection=None, backend=None, labeller=None, aes_by_visuals=None, visuals=None, stats=None, **pc_kwargs)

Bayes Factor for comparing hypothesis of two nested models.

The Bayes factor is estimated by comparing a model (H1) against a model in which the parameter of interest has been restricted to be a point-null (H0) This computation assumes H0 is a special case of H1. For more details see here https://arviz-devs.github.io/EABM/Chapters/Model_comparison.html#savagedickey-ratio

Parameters:

dtxarray.DataTree or dict of {strxarray.DataTree}

Input data. In case of dictionary input, the keys are taken to be model names. In such cases, a dimension “model” is generated and can be used to map to aesthetics.

var_namesstr, optional

Variables for which the bayes factor will be computed and the prior and posterior will be plotted.

ref_valint or float, default 0

Reference (point-null) value for Bayes factor estimation.

kind{“kde”, “hist”, “dot”, “ecdf”}, optional

How to represent the marginal density. Defaults to rcParams["plot.density_kind"]

sample_dimsstr or sequence of hashable, optional

Dimensions to reduce unless mapped to an aesthetic. Defaults to rcParams["data.sample_dims"]

plot_collectionPlotCollection, optional

backend{“matplotlib”, “bokeh”, “plotly”}, optional

labellerlabeller, optional

aes_by_visualsmapping of {strsequence of str}, optional

Mapping of visuals to aesthetics that should use their mapping in plot_collection when plotted. Valid keys are the same as for visuals.

visualsmapping of {strmapping or False}, optional

Valid keys are:

• dist -> depending on the value of kind passed to:

  • “kde” -> passed to line_xy

  • “ecdf” -> passed to ecdf_line

  • “hist” -> passed to :func: hist

• ref_line -> passed to :func: vline

• title -> passed to labelled_title

• legend -> passed to arviz_plots.PlotCollection.add_legend

statsmapping, optional

Valid keys are:

• dist -> passed to kde, ecdf, …

**pc_kwargs

Passed to arviz_plots.PlotCollection.wrap

Returns:

PlotCollection

Examples

Select one variable.

>>> from arviz_plots import plot_bf, style
>>> style.use("arviz-variat")
>>> from arviz_base import load_arviz_data
>>> dt = load_arviz_data('centered_eight')
>>> plot_bf(dt, var_names="mu", kind="hist")

Compute Bayes factor using Savage–Dickey ratio. We can apply this function when the null model is nested within the alternative. In other words when the null (ref_val) is a particular value of the model we are building (see [here](https://statproofbook.github.io/P/bf-sddr.html)). For others cases computing Bayes factor is not straightforward and requires more complex methods. Instead, of Bayes factors, we usually recommend Pareto smoothed importance sampling leave one out cross validation (PSIS-LOO-CV). In ArviZ, you will find them as functions with loo in their names.

Bayes_factor

Bayes_factor