Method predict_mean_and_var

gpflow/likelihoods/base.py:141–168

Given a Normal distribution for the latent function, return the mean and marginal variance of Y, i.e. if q(f) = N(Fmu, Fvar) and this object represents p(y|f) then this method computes the predictive mean ∫∫ y p(y|f)q(f

(
        self, X: TensorType, Fmu: TensorType, Fvar: TensorType
    )

        "return[1]: [batch..., observation_dim]",
    )
    def predict_mean_and_var(
        self, X: TensorType, Fmu: TensorType, Fvar: TensorType
    ) -> MeanAndVariance:
        """
        Given a Normal distribution for the latent function,
        return the mean and marginal variance of Y,

        i.e. if
            q(f) = N(Fmu, Fvar)

        and this object represents

            p(y|f)

        then this method computes the predictive mean

           ∫∫ y p(y|f)q(f) df dy

        and the predictive variance

           ∫∫ y² p(y|f)q(f) df dy  - [ ∫∫ y p(y|f)q(f) df dy ]²

        :param X: input tensor
        :param Fmu: mean function evaluation tensor
        :param Fvar: variance of function evaluation tensor
        :returns: mean and variance
        """
        return self._predict_mean_and_var(X, Fmu, Fvar)

    @abc.abstractmethod
    @check_shapes(