Sample N trajectories from the posterior. 
        
        Note
        ----
        Performs the forward step in case it has not been performed.

def sample(self, N=1):
        """Sample N trajectories from the posterior. 
        
        Note
        ----
        Performs the forward step in case it has not been performed. 

        """
        if not self.filt:
            self.forward()
        paths = np.empty((len(self.filt), N), np.int)
        paths[-1, :] = rs.multinomial(self.filt[-1], M=N)
        log_trans = np.log(self.hmm.trans_mat)
        for t, f in reversed(list(enumerate(self.filt[:-1]))):
            for n in range(N):
                probs = rs.exp_and_normalise(log_trans[:, paths[t + 1, n]] + np.log(f))
                paths[t, n] = rs.multinomial_once(probs)
        return paths