code: def model_prior(self, t):
          
  
          lg, test_ll = self._forward_backward(np.zeros((t, self.n_events)))
          segments = np.exp(lg)
  
          return segments, test_ll
docs: Returns the prior probability of the HMM
  
          Runs forward-backward without any data, showing the prior distribution
          of the model (for comparison with a posterior).
  
          Parameters
          ----------
          t: int
              Number of timepoints
  
          Returns
          -------
          segments : time by event ndarray
              segments[t,e] = prior probability that timepoint t is in event e
  
          test_ll : float
              Log-likelihood of model (data-independent term)