Returns likelihood of the states given the variance latent variables

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        alpha : np.array
            State matrix
        
        Returns
        ----------
        State likelihood

def state_likelihood(self, beta, alpha):
        """ Returns likelihood of the states given the variance latent variables

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        alpha : np.array
            State matrix
        
        Returns
        ----------
        State likelihood
        """
        _, _, _, Q = self._ss_matrices(beta)
        residuals_1 = alpha[0][1:alpha[0].shape[0]]-alpha[0][0:alpha[0].shape[0]-1]
        residuals_2 = alpha[1][1:alpha[1].shape[0]]-alpha[1][0:alpha[1].shape[0]-1]
        return np.sum(ss.norm.logpdf(residuals_1,loc=0,scale=np.power(Q[0][0],0.5))) + np.sum(ss.norm.logpdf(residuals_2,loc=0,scale=np.power(Q[1][1],0.5)))