diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py index 95b4ca0500..8afc733d67 100644 --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -711,16 +711,19 @@ def loo(data, pointwise=None, var_name=None, reff=None, scale=None): Returns ------- ELPDData object (inherits from :class:`pandas.Series`) with the following row/attributes: - elpd: approximated expected log pointwise predictive density (elpd) + elpd_loo: approximated expected log pointwise predictive density (elpd) se: standard error of the elpd p_loo: effective number of parameters - shape_warn: bool - True if the estimated shape parameter of Pareto distribution is greater than a thresold - value for one or more samples. For a sample size S, the thresold is compute as - min(1 - 1/log10(S), 0.7) - loo_i: array of pointwise predictive accuracy, only if pointwise True + n_samples: number of samples + n_data_points: number of data points + warning: bool + True if the estimated shape parameter of Pareto distribution is greater than + ``good_k``. + loo_i: :class:`~xarray.DataArray` with the pointwise predictive accuracy, + only if pointwise=True pareto_k: array of Pareto shape values, only if pointwise True scale: scale of the elpd + good_k: For a sample size S, the thresold is compute as min(1 - 1/log10(S), 0.7) The returned object has a custom print method that overrides pd.Series method. @@ -1580,7 +1583,9 @@ def waic(data, pointwise=None, var_name=None, scale=None, dask_kwargs=None): elpd_waic: approximated expected log pointwise predictive density (elpd) se: standard error of the elpd p_waic: effective number parameters - var_warn: bool + n_samples: number of samples + n_data_points: number of data points + warning: bool True if posterior variance of the log predictive densities exceeds 0.4 waic_i: :class:`~xarray.DataArray` with the pointwise predictive accuracy, only if pointwise=True diff --git a/doc/source/conf.py b/doc/source/conf.py index 71fa5d223d..04bac6e3f5 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -324,8 +324,6 @@ intersphinx_mapping = { "xarray": ("https://docs.xarray.dev/en/stable/", None), "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), - # Oriol: I am lazy and didn't want to update any references we might have using the pymc3 key - "pymc3": ("https://www.pymc.io/projects/docs/en/stable", None), "pymc": ("https://www.pymc.io/projects/docs/en/stable", None), "mpl": ("https://matplotlib.org/stable", None), "bokeh": ("https://docs.bokeh.org/en/latest/", None),