Hi All, once again, my apologies for a (possibly) very ignorant question, my google-fu is failing me... also because I am not sure of what exactly I should look for.
My problem is relatively simple. Let's assume I have two Python objects, A and B, and one of their attributes can assume a value of "True" or "False" depending on the results of a uniform random distribution sample, i.e.: probability_A = 0.95 probability_B = 0.86 A.has_failed = False B.has_failed = False if numpy.random.random() < probability_A: A.has_failed = True if numpy.random.random() < probability_B: B.has_failed = True Now, I know that there is a correlation factor between the failing/not failing of A and the failing/not failing of B. Specifically, If A fails, then B should have 80% more chance of failing, but I have been banging my head to find out how I should modify the "probability_B" number (or the extremes of the uniform distribution, if that makes sense) in order to reflect that correlation. I have been looking at correlated distributions, but it appears that most of the results I have found relate to normal distributions, there is very little about non-normal (and especially uniform) distributions. It's also very likely that I am not looking in the right direction, so I would appreciate any suggestion you may share. Thank you in advance. Andrea. "Imagination Is The Only Weapon In The War Against Reality." http://xoomer.alice.it/infinity77/ _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion