code: def conditional_probability_alive(self, frequency, recency, T):
          
          r, alpha, a, b = self._unload_params("r", "alpha", "a", "b")
          return np.atleast_1d(1.0 / (1 + (a / (b + frequency)) * ((alpha + T) / (alpha + recency)) ** (r + frequency)))
docs: Conditional probability alive.
  
          Compute the probability that a customer with history (frequency,
          recency, T) is currently alive.
          From https://www.researchgate.net/publication/247219660_Empirical_validation_and_comparison_of_models_for_customer_base_analysis
          Appendix A, eq. (5)
  
          Parameters
          ----------
          frequency: array or float
              historical frequency of customer.
          recency: array or float
              historical recency of customer.
          T: array or float
              age of the customer.
  
          Returns
          -------
          array:
              value representing probability of being alive