def map_forecast_estimate(self):
        """ get the prior and posterior forecast (prediction) expectations.

        Returns
        -------
        pandas.DataFrame : pandas.DataFrame
            dataframe with prior and posterior forecast expected values

        """
        assert self.forecasts is not None
        islog = self.pst.parameter_data.partrans == "log"
        par_map = self.map_parameter_estimate
        par_map.loc[islog,:] = np.log10(par_map.loc[islog,:])
        par_map = Matrix.from_dataframe(par_map.loc[:,["post_expt"]])
        posts,priors = [],[]
        post_expt = (self.predictions.T * par_map).to_dataframe()
        for fname in self.forecast_names:
            #fname = forecast.col_names[0]
            pr = self.pst.res.loc[fname,"modelled"]
            priors.append(pr)
            posts.append(pr + post_expt.loc[fname,"post_expt"])
        return pd.DataFrame(data=np.array([priors,posts]).transpose(),
                            columns=["prior_expt","post_expt"],
                            index=self.forecast_names)
get the prior and posterior forecast (prediction) expectations.

        Returns
        -------
        pandas.DataFrame : pandas.DataFrame
            dataframe with prior and posterior forecast expected values