def mom(self, K, **kws):
        """
        Raw statistical moments.

        Creates non-centralized raw moments from the random variable. If
        analytical options can not be utilized, Monte Carlo integration
        will be used.

        Args:
            K (numpy.ndarray):
                Index of the raw moments. k.shape must be compatible with
                distribution shape.  Sampling scheme when performing Monte
                Carlo
            rule (str):
                rule for estimating the moment if the analytical method fails.
            composite (numpy.ndarray):
                If provided, composit quadrature will be used.  Ignored in the
                case if gaussian=True.  If int provided, determines number of
                even domain splits. If array of ints, determines number of even
                domain splits along each axis. If array of arrays/floats,
                determines location of splits.
            antithetic (numpy.ndarray):
                List of bool. Represents the axes to mirror using antithetic
                variable during MCI.

        Returns:
            (numpy.ndarray):
                Shapes are related through the identity
                ``k.shape == dist.shape+k.shape``.
        """
        K = numpy.asarray(K, dtype=int)
        shape = K.shape
        dim = len(self)

        if dim > 1:
            shape = shape[1:]

        size = int(K.size/dim)
        K = K.reshape(dim, size)

        cache = {}
        out = [evaluation.evaluate_moment(self, kdata, cache) for kdata in K.T]
        out = numpy.array(out)
        return out.reshape(shape)
Raw statistical moments.

        Creates non-centralized raw moments from the random variable. If
        analytical options can not be utilized, Monte Carlo integration
        will be used.

        Args:
            K (numpy.ndarray):
                Index of the raw moments. k.shape must be compatible with
                distribution shape.  Sampling scheme when performing Monte
                Carlo
            rule (str):
                rule for estimating the moment if the analytical method fails.
            composite (numpy.ndarray):
                If provided, composit quadrature will be used.  Ignored in the
                case if gaussian=True.  If int provided, determines number of
                even domain splits. If array of ints, determines number of even
                domain splits along each axis. If array of arrays/floats,
                determines location of splits.
            antithetic (numpy.ndarray):
                List of bool. Represents the axes to mirror using antithetic
                variable during MCI.

        Returns:
            (numpy.ndarray):
                Shapes are related through the identity
                ``k.shape == dist.shape+k.shape``.