Joe Marasco is the author of The Software Development Edge: Essays on Managing Successful Projects, published by Addison Wesley in 2005. His interests include modeling organizational behavior to improve performance and profitability. He can be reached at email@example.com.
In this paper, Joe introduces us to the application of Bayesian theory to assess how we are doing on our project after we have started — in fact at our first milestone. Essentially, Bayes consists of two pieces: A prior probability, and some new information. Combining those two gives you a new probability. The crucial observation is that this can be applied as an iterative process, with each new probability becoming the prior probability for the subsequent iteration. In essence, it is a bootstrapping process that can be repeated through the project's life span.
True, that Bayesian theory is subject to the criticism: "Where do you get the first (i.e., prior) probability in the first place?" If that is nothing better than a poor guess, then garbage in, garbage out is claimed. However, experience shows that in most cases the result after several iterations is insensitive to the original estimate anyway, because the new data quickly adjusts the probability for us if the subsequent tests are of good quality. But "good" project managers interested in applying Bayesian theory will have made sure that they have a good baseline estimate and plan to work with in the first place, so that should not be a problem.