Thanks for the enlightenment, George.
I misinterpreted what was said in Numerical Recipes, where it starts
by
referring to the Box-Muller method, then gives your algorithm without
any
intermediate referral. Hence I had always thought of this method as
being B-M.
Hey, I learnt something new, can
The Box-Muller algorithm rejects roughly 22.5% of the
generated points. I'm not aware of any bound on the number
of consecutive rejections, other than a statistical one, hence
my statement. I would welcome correction if this is not the case.
Regards
Ian
Radford Neal [EMAIL PROTECTED] wrote
Ian Buckner [EMAIL PROTECTED] wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
The Box-Muller algorithm rejects roughly 22.5% of the
generated points. I'm not aware of any bound on the number
of consecutive rejections, other than a statistical one, hence
my statement. I would
Generated on custom silicon (surprise).
Box-Muller does not work for real time requirements.
Ian
Glen Barnett [EMAIL PROTECTED] wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
Ian Buckner wrote:
We generate pairs of properly distributed Gaussian variables at
down to 10nsec
Box-Muller does not work for real time requirements.
This isn't true, of course. A real time application is one where
one must guarantee that an operation takes no more than some specified
maximum time. The Box-Muller method for generating normal random
variates does not involve any operations
In article [EMAIL PROTECTED],
Radford Neal [EMAIL PROTECTED] wrote:
Box-Muller does not work for real time requirements.
This isn't true, of course. A real time application is one where
one must guarantee that an operation takes no more than some specified
maximum time. The Box-Muller method
Ian Buckner [EMAIL PROTECTED] wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
Glen Barnett [EMAIL PROTECTED] wrote in message
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Ian Buckner wrote:
We generate pairs of properly distributed Gaussian variables at
down to 10nsec
Herman Rubin [EMAIL PROTECTED] wrote in message
a4u99j$[EMAIL PROTECTED]">news:a4u99j$[EMAIL PROTECTED]...
In article [EMAIL PROTECTED],
Radford Neal [EMAIL PROTECTED] wrote:
Box-Muller does not work for real time requirements.
This isn't true, of course. A real time application is one
We generate pairs of properly distributed Gaussian variables at
down to 10nsec intervals, essential in the application. Speed can
be an issue, particularly in real time situations.
Ian
Glen Barnett [EMAIL PROTECTED] wrote in message
a4plof$p3s$[EMAIL PROTECTED]">news:a4plof$p3s$[EMAIL
At 09:50 AM 2/18/02 +, Ian Buckner wrote:
We generate pairs of properly distributed Gaussian variables at
down to 10nsec intervals, essential in the application. Speed can
be an issue, particularly in real time situations.
Ian
wow ... how our perspectives have changed! back in grad school,
Ian Buckner wrote:
We generate pairs of properly distributed Gaussian variables at
down to 10nsec intervals, essential in the application. Speed can
be an issue, particularly in real time situations.
Generated on what? (On a fast enough machine, even clunky old Box-Muller
can probably give
Alan Miller [EMAIL PROTECTED] wrote in message
OC2b8.28457$[EMAIL PROTECTED]">news:OC2b8.28457$[EMAIL PROTECTED]...
First - the reference to George's paper on the ziggurat, and the code:
The Journal of Statistical Software (2000) at:
http://www.jstatsoft.org/v05/i08
That I already have,
Bob Wheeler [EMAIL PROTECTED] wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
Marsaglia's ziggurat and MCW1019 generators are
available in the R package SuppDists. The gcc
compiler was used.
Thanks Bob.
Glen
=
George Marsaglia [EMAIL PROTECTED] wrote in message
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(3-year old) Timings, in nanoseconds, using Microsoft Visual C++
and gcc under DOS on a 400MHz PC. Comparisons are with
methods by Leva and by Ahrens-Dieter, both said
Art Kendall [EMAIL PROTECTED] wrote in message
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I tend to be more concerned with the apparent randomness of the results
than with the speed of the algorithm.
This will be mainly a function of the randomness of the uniform generator. If
we assume the
Glen [EMAIL PROTECTED] wrote in message
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Alan Miller [EMAIL PROTECTED] wrote in message
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The fastest way to generate random normals and exponentials is to use George
Marsaglia's ziggurat algorithm.
I've seen
Marsaglia's ziggurat and MCW1019 generators are
available in the R package SuppDists. The gcc
compiler was used.
George Marsaglia wrote:
Glen [EMAIL PROTECTED] wrote in message
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Alan Miller [EMAIL PROTECTED] wrote in message
I tend to be more concerned with the apparent randomness of the results than with
the speed of the algorithm.
As a thought experiment, what is the cumulative time difference in a run using the
fastest vs the slowest algorithm? A
whole minute? A second? A fractional second?
Glen wrote:
Alan
Art Kendall [EMAIL PROTECTED] wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
I tend to be more concerned with the apparent randomness of the results than with
the speed
of the algorithm.
As a thought experiment, what is the cumulative time difference in a run using the
George Marsaglia wrote in message
0l7b8.42092$[EMAIL PROTECTED]...
.. chunk deleted
The Monty Python method is not quite as fast as as the Ziggurat.
Some may think that Alan Miller's somewhat vague reference to
a source for the ziggurat article suggests disdain. The source is
Journal of
Alan Miller [EMAIL PROTECTED] wrote in message
news:K1Fa8.25709$[EMAIL PROTECTED]...
The fastest way to generate random normals and exponentials is to use George
Marsaglia's ziggurat algorithm.
I've seen both ziggurat and Monty Python approaches claimed as being
about the fastest or close
Glen wrote in message ...
Alan Miller [EMAIL PROTECTED] wrote in message
news:K1Fa8.25709$[EMAIL PROTECTED]...
The fastest way to generate random normals and exponentials is to use
George
Marsaglia's ziggurat algorithm.
I've seen both ziggurat and Monty Python approaches claimed as being
about
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