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MathGroup Archive 2002

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simulating random variables

  • To: mathgroup at smc.vnet.net
  • Subject: [mg32831] simulating random variables
  • From: "Aaron E. Hirsh" <aehirsh at stanford.edu>
  • Date: Thu, 14 Feb 2002 01:44:02 -0500 (EST)
  • Sender: owner-wri-mathgroup at wolfram.com

Dear All,

I need to simulate a large number of binomial random variables. 
Unfortunately, when the parameter n (number of trials) is large, the 
simulation of binomial random variables is relatively time consuming. 
For example, if I would like to simulate a binomial random variable 
with parameters n = 10000 and p =0.01:

In[134]:=
Timing[Random[BinomialDistribution[100000,0.01]]]

Out[134]=
{0.283333 Second,1007}

One possibility for saving time would be to use an approximation. For 
small p, an appropriate approximation is the Poisson. While this is 
definitely better:

In[135]:=
Timing[Random[PoissonDistribution[(.01)(100000)]]]

Out[135]=
{0.0333333 Second,1002}

, it is still much slower than using a normal:

In[136]:=
\!\(Timing[
     Random[NormalDistribution[(.01)(100000),((.01)(100000)(.99))^0.5]]]\)

Out[136]=
{0. Second,1025.3}

Does anyone know how I could speed up my simulation of binomial or 
poisson-distributed random variables? I would also be interested in 
ways of speeding up the simulation of the Normal, though it seems 
extraordinarily efficient already.

     Thank you very much,

Aaron Hirsh
-- 

Aaron E. Hirsh
Center for Computational Genetics and Biological Modeling
Stanford University
tel. (650) 723-4952
fax. (650) 725-0180


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