Prof Brian Ripley wrote:
Your coincidence calculations may be correct for _independent_ draws from a
discrete distribution on M values, but independence is not satisfied.
Yet again, you are trying to do things that any good text on simulation
would warn you against, and which (in a thread on
In fact, that is what I saw in your RNG help in R 2.7.1:
'"Wichmann-Hill"' The seed, '.Random.seed[-1] == r[1:3]' is an
integer vector of length 3, where each 'r[i]' is in '1:(p[i]
- 1)', where 'p' is the length 3 vector of primes, 'p =
(30269, 30307, 30323)'. The
Prof Brian Ripley wrote:
Your coincidence calculations may be correct for _independent_ draws
from a discrete distribution on M values, but independence is not
satisfied.
Yet again, you are trying to do things that any good text on
simulation would warn you against, and which (in a thread on R-
Please don't lie! You falsely claimed:
In R version 2.7.1, Brian Ripley adopted Wichmann's 1984 correction in
RNG document.
The period in the help has been unchanged since at least Jan 2000 (since
Random.Rd has not had any changes to those lines since then).
Your coincidence calculations m
On Sun, 17 Aug 2008, Duncan Murdoch wrote:
Shengqiao Li wrote:
Dear all,
Recently I am generating large random samples (10M) and any duplicated
numbers are not desired.
We tried several RNGs in R and found Wichmann-Hill did not produce
duplications.
The duplication problem is the interest
Shengqiao Li wrote:
Dear all,
Recently I am generating large random samples (10M) and any duplicated
numbers are not desired.
We tried several RNGs in R and found Wichmann-Hill did not produce
duplications.
The duplication problem is the interesting birthday problem. If there are
M possible
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