On Sun, Aug 16, 2009 at 6:13 PM, Johannes Schindelin<Johannes.Schindelin@xxxxxx> wrote: > I tried to find some documentation for Bayesian search theory, but it > seems those ridiculous Wikipedia admins struck once again, in their > mission to reduce the world's intellect to their own. It looks like it is still there to me: http://en.wikipedia.org/wiki/Bayesian_search_theory It looks like github has included a ')' on the end when html-ifying the link inthe README, making it into a dead link. I'll fix that. The wikipedia article is still not amazing,though. Unfortunately most of the online descriptions of Bayesian Search Theory, such as: http://www.sarinz.com/index.cfm/3,112,261/landsearchmethodsreview.pdf seem to go heavily into the minutia of search-and-rescue, which while interesting, is not relevant to git. However, although I got the idea of bbchop from search theory, it is not necessary to know much of search theory in order to understand bbchop. The basic algorithm is very simple: At each step, test the commit for which the expected gain of information (about the location of the bug) is greatest. That is basically all I got from search theory so far - the calculation of the probability of the bug existing in each location is standard bayesian probability theory, which maybe you already know. If not, a very readable reference is: http://www.inference.phy.cam.ac.uk/mackay/itila/book.html (free on-line book). So all the code does is compute N entropies and pick the best. Most of the complexity is introduced by: - calculating the N entropies without calculating N^2 probabilities - calculations over a DAG. Ealdwulf -- To unsubscribe from this list: send the line "unsubscribe git" in the body of a message to majordomo@xxxxxxxxxxxxxxx More majordomo info at http://vger.kernel.org/majordomo-info.html