Thanks for the excellent statistics library, especially the on-line algorithms for the statistics object. However, I often need to partition a large population into subsets, obtain the statistics of each subset, and obtain the statistics of various unions of the subsets as well as for the entire population. In the past (and in other languages not as fun as Racket) I've done this using Chan's parallel algorithm for the mean and variance and Terriberry's extension of Chan's algorithm for skewness and kurtosis, so that I can keep running statistics on each disjoint subset and later combine them for the various aggregations.
I'd like to similarly extend math/statistics to enable the summation of the running statistics. However, I'm not very statistics-savvy and so I'm having trouble following the algorithm in the update-statistics function that handles weighted samples. I also don't have ready access to Pébaÿ's recent papers that extend Terriberry's method to handle weighted samples, and I probably wouldn't understand Pébaÿ's paper anyway :-( So on to my questions: Is the algorithm used in the update-statistics function amenable to being extended to a parallel version? In other words, do the moments in the statistics structure correspond to the first through fourth central moments? Is anyone here familiar with the extensions to the parallel algorithms that handle weighted samples? I'd like to support weighted samples, but I just don't know how to handle them. Best regards, -Steve -- Steve Byan steveb...@me.com Littleton, MA -- You received this message because you are subscribed to the Google Groups "Racket Users" group. To unsubscribe from this group and stop receiving emails from it, send an email to racket-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
