Are you against adding any nextSample() method to distributions at all (regardless of the quality of the implementation)?
Or just unhappy about adding nextSample() hooked to a bad implementation? The first opinion, I just don't understand. The second can be dealt with by putting in good implementations or by throwing UOE. I have a little bit of sympathy as a developer for separating all sampling from the distributions, but I have no sympathy at all with this as a user. I think of a distribution as something that you can take the density of, (often) get the cumulative distribution from and get a sample from. I know in my heart of hearts that there is something down deep that is probably called a <mumble>DistributionGenerator. I even know that underneath that, there is likely to be a uniform distribution generator. What what I think about when using a system is "sampling from a distribution" just like anybody trained in statistics would. That means that I expect <mumble>Distribution.nextSample() to exist. I know that it might be fast or slow, but having hunted up the distribution I want, I *don't* want to have to imagine what class might generate the distribution I want. The key here is what a user of the system thinks. Not how an implementor thinks. On Mon, Oct 26, 2009 at 6:01 PM, Phil Steitz <phil.ste...@gmail.com> wrote: > What I > am -1 on is adding (potentially poor) random data generation to the > distributions implementations. > -- Ted Dunning, CTO DeepDyve
