On Thu, Aug 16, 2012 at 3:27 PM, Andrea Gavana <andrea.gav...@gmail.com>wrote:
> 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. > easiest, I guess, is to work with a discrete distribution with 4 states, where states reflect the joint event (a, b) True, True True, False ... Then you have 3 probabilities to choose any amount of dependence, and marginal probabilities. (more complicated, correlated Probit) to generate random numbers, a recipe of Charles on the mailing list, or a new version of numpy might be helpful. Josef > > 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 >
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