code: def AIC(N, rho, k):
      r
      from numpy import log, array
      #k+1 #todo check convention. agrees with octave
  
      res = N * log(array(rho)) + 2.* (array(k)+1)
      return res
docs: r"""Akaike Information Criterion
  
      :param rho: rho at order k
      :param N: sample size
      :param k: AR order.
  
      If k is the AR order and N the size of the sample, then Akaike criterion is
  
      .. math:: AIC(k) = \log(\rho_k) + 2\frac{k+1}{N}
  
      ::
  
          AIC(64, [0.5,0.3,0.2], [1,2,3])
  
      :validation: double checked versus octave.