Hi,
I am constructing a function that does sampling in C++ using a non-R RNG
stream for thread safety reasons. This C++ function is wrapped by an R
function, which is user facing. The R wrapper does some sampling itself to
initialize some variables before passing them off to C++. So that my users
I wouldn't trust the C++ generator to be as good if you seed it this way
as if you just seeded it once with your phone number (or any other fixed
value) and let it run, because it's probably never been tested to be
good when run this way. Is it good enough for the way you plan to use
it? Mayb
Thank you for this. I'd like to be sure I understand the
intuition correctly. Is the following true from what you said?
I can just fix the seed at the C++ level and the results will still be
(pseudo) random because the initialization at the R level is (pseudo)
random.
On Thu, Jul 30, 2020 at 3:36
Tommy,
I'm not Duncan (and am not nor claim to be an RNG expert) but I believe RNG
streams are designed and thus tested, to be used as streams. Repeatedly
setting the seed after small numbers of samples from them does not fit the
designed usecase (And also doesn't match the test criteria by which
On 30/07/2020 4:30 p.m., Tommy Jones wrote:
Thank you for this. I'd like to be sure I understand the
intuition correctly. Is the following true from what you said?
I can just fix the seed at the C++ level and the results will still be
(pseudo) random because the initialization at the R level i
Thank you Duncan and Gabriel.
I think that my trivial example was a little too trivial and is causing
some confusion. What's happening in the real function I'm writing is...
1. In R: Draw tens-of-thousands of times from a handful to Gamma RVs with
different parameters to initialize some variables
> 3. In C++: Draw millions of times from a Categorical(p) distribution, where
> "p" is recalculated after each draw
I don't see the need here.
It should be possible to generate all the random numbers , *in R*, and
in *one line* of R code.
Easy...
Then standard inversion sampling, can be used to t
Abby, that is a fantastic suggestion! It seems obvious now that you've said
it. Why didn't I think of that?
Thank you,
Tommy
On Fri, Jul 31, 2020 at 12:01 AM Abby Spurdle wrote:
> > 3. In C++: Draw millions of times from a Categorical(p) distribution,
> where
> > "p" is recalculated after each
