El mié., 19 sept. 2018 a las 14:43, Duncan Murdoch (<murdoch.dun...@gmail.com>) escribió: > > On 18/09/2018 5:46 PM, Carl Boettiger wrote: > > Dear list, > > > > It looks to me that R samples random integers using an intuitive but biased > > algorithm by going from a random number on [0,1) from the PRNG to a random > > integer, e.g. > > https://github.com/wch/r-source/blob/tags/R-3-5-1/src/main/RNG.c#L808 > > > > Many other languages use various rejection sampling approaches which > > provide an unbiased method for sampling, such as in Go, python, and others > > described here: https://arxiv.org/abs/1805.10941 (I believe the biased > > algorithm currently used in R is also described there). I'm not an expert > > in this area, but does it make sense for the R to adopt one of the unbiased > > random sample algorithms outlined there and used in other languages? Would > > a patch providing such an algorithm be welcome? What concerns would need to > > be addressed first? > > > > I believe this issue was also raised by Killie & Philip in > > http://r.789695.n4.nabble.com/Bug-in-sample-td4729483.html, and more > > recently in > > https://www.stat.berkeley.edu/~stark/Preprints/r-random-issues.pdf, > > pointing to the python implementation for comparison: > > https://github.com/statlab/cryptorandom/blob/master/cryptorandom/cryptorandom.py#L265 > > I think the analyses are correct, but I doubt if a change to the default > is likely to be accepted as it would make it more difficult to reproduce > older results. > > On the other hand, a contribution of a new function like sample() but > not suffering from the bias would be good. The normal way to make such > a contribution is in a user contributed package. > > By the way, R code illustrating the bias is probably not very hard to > put together. I believe the bias manifests itself in sample() producing > values with two different probabilities (instead of all equal > probabilities). Those may differ by as much as one part in 2^32. It's
According to Kellie and Philip, in the attachment of the thread referenced by Carl, "The maximum ratio of selection probabilities can get as large as 1.5 if n is just below 2^31". Iñaki > very difficult to detect a probability difference that small, but if you > define the partition of values into the high probability values vs the > low probability values, you can probably detect the difference in a > feasible simulation. > > Duncan Murdoch > ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
