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
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
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