code: def _log_prior_transit(theta, priorbounds):
      '''
      Assume priors on all parameters have uniform probability.
      '''
      # priorbounds contains the input priors, and because of how we previously
      # sorted theta, its sorted keys tell us which parts of theta correspond to
      # which physical quantities.
  
      allowed = True
      for ix, key in enumerate(np.sort(list(priorbounds.keys()))):
          if priorbounds[key][0] < theta[ix] < priorbounds[key][1]:
              allowed = True and allowed
          else:
              allowed = False
  
      if allowed:
          return 0.
  
      return -np.inf _log_prior_transit(theta, priorbounds):
      '''
      Assume priors on all parameters have uniform probability.
      '''
      # priorbounds contains the input priors, and because of how we previously
      # sorted theta, its sorted keys tell us which parts of theta correspond to
      # which physical quantities.
  
      allowed = True
      for ix, key in enumerate(np.sort(list(priorbounds.keys()))):
          if priorbounds[key][0] < theta[ix] < priorbounds[key][1]:
              allowed = True and allowed
          else:
              allowed = False
  
      if allowed:
          return 0.
  
      return -np.inf
docs: Assume priors on all parameters have uniform probability.