I an very interested in this framework so I have started to take a look. General Accumulators are something I could make use of myself. I really like the conceptual design of the framework, and how it allows accumulators to be inter-dependant.. After a quick browse through the documentation I decided to take a look at the code. In particular I was interested in the numerics. I think the current implementation has some serious numerical weaknesses. I looked at 2 algorithms 'sum' and 'variance': In 'sum' I expected to see a compensated summation, this is numerically a lot better then just adding the numbers together. The 'variance' accumulator has a lazy calculation of variance using the formula \sigma_n^2 = M_n^{(2)} - \mu_n^2. This formal is specifically cited for it poor performance in the presence of rounding error. Indeed it may even return negative results. Any chance of getting your statistics guys to take a look at the numerics of the solutions? If people were to use library as is they would be in for nasty surprised! All the best, Michael -- ___________________________________ Michael Stevens Systems Engineering 34128 Kassel, Germany Phone/Fax: +49 561 5218038 Navigation Systems, Estimation and Bayesian Filtering http://bayesclasses.sf.net ___________________________________