def confidence_interval(self, alpha=0.9):
        """Compute confidence interval on accumulated metric values

        Parameters
        ----------
        alpha : float, optional
            Probability that the returned confidence interval contains
            the true metric value.

        Returns
        -------
        (center, (lower, upper))
            with center the mean of the conditional pdf of the metric value
            and (lower, upper) is a confidence interval centered on the median,
            containing the estimate to a probability alpha.

        See Also:
        ---------
        scipy.stats.bayes_mvs

        """
        m, _, _ = scipy.stats.bayes_mvs(
            [r[self.metric_name_] for _, r in self.results_], alpha=alpha)
        return m
Compute confidence interval on accumulated metric values

        Parameters
        ----------
        alpha : float, optional
            Probability that the returned confidence interval contains
            the true metric value.

        Returns
        -------
        (center, (lower, upper))
            with center the mean of the conditional pdf of the metric value
            and (lower, upper) is a confidence interval centered on the median,
            containing the estimate to a probability alpha.

        See Also:
        ---------
        scipy.stats.bayes_mvs