def predict_moments(self, X, nsamples=200, likelihood_args=()):
        r"""
        Predictive moments, in particular mean and variance, of a Bayesian GLM.

        This function uses Monte-Carlo sampling to evaluate the predictive mean
        and variance of a Bayesian GLM. The exact expressions evaluated are,

        .. math ::

            \mathbb{E}[y^* | \mathbf{x^*}, \mathbf{X}, y] &=
                \int \mathbb{E}[y^* | \mathbf{w}, \phi(\mathbf{x}^*)]
                p(\mathbf{w} | \mathbf{y}, \boldsymbol\Phi) d\mathbf{w},

            \mathbb{V}[y^* | \mathbf{x^*}, \mathbf{X}, y] &=
                \int \left(\mathbb{E}[y^* | \mathbf{w}, \phi(\mathbf{x}^*)]
                - \mathbb{E}[y^* | \mathbf{x^*}, \mathbf{X}, y]\right)^2
                p(\mathbf{w} | \mathbf{y}, \boldsymbol\Phi) d\mathbf{w},

        where :math:`\mathbb{E}[y^* | \mathbf{w}, \phi(\mathbf{x}^*)]` is the
        the expected value of :math:`y^*` from  the likelihood, and
        :math:`p(\mathbf{w} | \mathbf{y}, \boldsymbol\Phi)` is the posterior
        distribution over weights (from ``learn``). Here are few concrete
        examples of how we can use these values,

        - Gaussian likelihood: these are just the predicted mean and variance,
          see ``revrand.regression.predict``
        - Bernoulli likelihood: The expected value is the probability,
          :math:`p(y^* = 1)`, i.e. the probability of class one. The variance
          may not be so useful.
        - Poisson likelihood: The expected value is similar conceptually to the
          Gaussian case, and is also a *continuous* value. The median (50%
          quantile) from ``predict_interval`` is a discrete value. Again,
          the variance in this instance may not be so useful.

        Parameters
        ----------
        X : ndarray
            (N*,d) array query input dataset (N* samples, d
            dimensions).
        nsamples : int, optional
            Number of samples for sampling the expected moments from the
            predictive distribution.
        likelihood_args : sequence, optional
            sequence of arguments to pass to the likelihood function. These are
            non-learnable parameters. They can be scalars or arrays of length
            N.

        Returns
        -------
        Ey : ndarray
            The expected value of y* for the query inputs, X* of shape (N*,).
        Vy : ndarray
            The expected variance of y* (excluding likelihood noise terms) for
            the query inputs, X* of shape (N*,).
        """
        # Get latent function samples
        N = X.shape[0]
        ys = np.empty((N, nsamples))
        fsamples = self._sample_func(X, nsamples)

        # Push samples though likelihood expected value
        Eyargs = tuple(chain(atleast_list(self.like_hypers_), likelihood_args))
        for i, f in enumerate(fsamples):
            ys[:, i] = self.likelihood.Ey(f, *Eyargs)

        # Average transformed samples (MC integration)
        Ey = ys.mean(axis=1)
        Vy = ((ys - Ey[:, np.newaxis])**2).mean(axis=1)

        return Ey, Vy
def predict_moments ( self , X , nsamples = 200 , likelihood_args = ( ) ) : N = X . shape [ 0 ] ys = np . empty ( ( N , nsamples ) ) fsamples = self . _sample_func ( X , nsamples ) Eyargs = tuple ( chain ( atleast_list ( self . like_hypers_ ) , likelihood_args ) ) for i , f in enumerate ( fsamples ) : ys [ : , i ] = self . likelihood . Ey ( f , * Eyargs ) Ey = ys . mean ( axis = 1 ) Vy = ( ( ys - Ey [ : , np . newaxis ] ) ** 2 ) . mean ( axis = 1 ) return Ey , Vy
r"""
        Predictive moments, in particular mean and variance, of a Bayesian GLM.

        This function uses Monte-Carlo sampling to evaluate the predictive mean
        and variance of a Bayesian GLM. The exact expressions evaluated are,

        .. math ::

            \mathbb{E}[y^* | \mathbf{x^*}, \mathbf{X}, y] &=
                \int \mathbb{E}[y^* | \mathbf{w}, \phi(\mathbf{x}^*)]
                p(\mathbf{w} | \mathbf{y}, \boldsymbol\Phi) d\mathbf{w},

            \mathbb{V}[y^* | \mathbf{x^*}, \mathbf{X}, y] &=
                \int \left(\mathbb{E}[y^* | \mathbf{w}, \phi(\mathbf{x}^*)]
                - \mathbb{E}[y^* | \mathbf{x^*}, \mathbf{X}, y]\right)^2
                p(\mathbf{w} | \mathbf{y}, \boldsymbol\Phi) d\mathbf{w},

        where :math:`\mathbb{E}[y^* | \mathbf{w}, \phi(\mathbf{x}^*)]` is the
        the expected value of :math:`y^*` from  the likelihood, and
        :math:`p(\mathbf{w} | \mathbf{y}, \boldsymbol\Phi)` is the posterior
        distribution over weights (from ``learn``). Here are few concrete
        examples of how we can use these values,

        - Gaussian likelihood: these are just the predicted mean and variance,
          see ``revrand.regression.predict``
        - Bernoulli likelihood: The expected value is the probability,
          :math:`p(y^* = 1)`, i.e. the probability of class one. The variance
          may not be so useful.
        - Poisson likelihood: The expected value is similar conceptually to the
          Gaussian case, and is also a *continuous* value. The median (50%
          quantile) from ``predict_interval`` is a discrete value. Again,
          the variance in this instance may not be so useful.

        Parameters
        ----------
        X : ndarray
            (N*,d) array query input dataset (N* samples, d
            dimensions).
        nsamples : int, optional
            Number of samples for sampling the expected moments from the
            predictive distribution.
        likelihood_args : sequence, optional
            sequence of arguments to pass to the likelihood function. These are
            non-learnable parameters. They can be scalars or arrays of length
            N.

        Returns
        -------
        Ey : ndarray
            The expected value of y* for the query inputs, X* of shape (N*,).
        Vy : ndarray
            The expected variance of y* (excluding likelihood noise terms) for
            the query inputs, X* of shape (N*,).