'Updates the moments (x_hat, Sigma) of the time t prior to the
time t filtering distribution, using current measurement :math:`y_t`.
The updates are according to
.. math::
\hat{x}^F = \hat{x} + \Sigma G\' (G \Sigma G\' + R)^{-1}
(y - G \hat{x})
\Sigma^F = \Sigma - \Sigma G\' (G \Sigma G\' + R)^{-1} G
\Sigma
Parameters
y : scalar or array_like(float)
The current measurement'

def prior_to_filtered(self, y):

    (G, H) = (self.ss.G, self.ss.H)
    R = np.dot(H, H.T)
    y = np.atleast_2d(y)
    y.shape = (self.ss.k, 1)
    E = dot(self.Sigma, G.T)
    F = (dot(dot(G, self.Sigma), G.T) + R)
    M = dot(E, inv(F))
    self.x_hat = (self.x_hat + dot(M, (y - dot(G, self.x_hat))))
    self.Sigma = (self.Sigma - dot(M, dot(G, self.Sigma)))
