On Thu, Mar 12, 2009 at 6:02 PM, Steven Tweed <orthochronous@xxxxxxxxx> wrote: > I haven't even looked at the source code so a description of the > mathematical algorithm would help, but I'll just point out that > underflow (in the case of working with probabilities) and overflow > (when working with their negated logarithms) is inherent in most > multi-step Bayesian algorithms. The only solution is to rescale things > as you go so that things stay in a "computable" range. (You're almost > never interested in absolute probabilities anyway but rather relative > probabilities or, in extreme cases, just the biggest probability, so > rescaling isn't losing any useful information.) Are you sure you aren't thinking of when you are using fixed point? I was under the impression that Bayesian algorithms usually worked okay in floating point. One issue in BBChop which should be easy to fix, is that I use a dumb way of calculating Beta functions. These are ratios of factorials, so the subexpressions get stupidly big very quickly. But I don't think that is the only problem. 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
