Bruce Carneal did some tests of robustness and speed for various normal
generators. I don't know what his final tests showed for Box-Muller. IIRC,
it had some failures but nothing spectacular. The tests were pretty
stringent and based on using the erf to turn the normal distribution into a
Hello,
I have been reading that there may be potential issues with the
Box-Muller transform, which is used by the numpy.random.normal()
function. Supposedly, since f*x1 and f*x2 are not independent variables, then
the individual elements (corresponding to f*x1 and f*x2 ) of the
distribution
I think the use of a correct uniform generator will allow a good
normal distribution. Congruental generators are very basic generators,
everyone knows they should not be used. I think Numpy uses a Mersenne
Twisted generator, for which you can generate independant vectors
with several hundred
Wed, 10 Dec 2008 14:03:39 -0500, Michael Gilbert wrote:
I have been reading that there may be potential issues with the
Box-Muller transform, which is used by the numpy.random.normal()
function. Supposedly, since f*x1 and f*x2 are not independent
variables, then the individual elements
On Wed, Dec 10, 2008 at 12:03 PM, Michael Gilbert
[EMAIL PROTECTED] wrote:
Hello,
I have been reading that there may be potential issues with the
Box-Muller transform, which is used by the numpy.random.normal()
function. Supposedly, since f*x1 and f*x2 are not independent variables,
