code: def state_likelihood(self, beta, alpha):
          
          _, _, _, Q = self._ss_matrices(beta)
          state_lik = 0
          for i in range(alpha.shape[0]):
              state_lik += np.sum(ss.norm.logpdf(alpha[i][1:]-alpha[i][:-1],loc=0,scale=np.power(Q[i][i],0.5))) 
          return state_lik
docs: 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