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Felipe
Posts:
5
Registered:
9/27/12
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improving speed of simulation using random numbers
Posted:
Nov 15, 2012 4:03 AM
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Dear all,
I am trying to compute an expected value using simulation.
I have a random number x with density function d[x]. I want to compute the expected value of function f[x], which is equal to the integral of f[x] times
d[x] over x.
In my case, it is difficult to compute the integral so I simulate N values for x and compute the average of f[x] over all N simulated values.
My problem is that my code takes to long for my purposes: this is a part of a larger program and is making it unfeasible in terms of time.
The following code provides an example of the situation, and my question is how could I reduce the time this takes. THanks a lot for your help
g[x_]:= x^2
mydensity[myparameter_]:= ProbabilityDistribution[myparameter*(t)^(-myparameter - 1), {t, 1, Infinity}]
randomnum[myparameter_] := RandomVariate[draw[myparameter], 50]
Timing[Sum[g[randomnum[5][[i]]], {i, 1, 50}]]
Out[1353]= {0.64, 81.7808}
This takes 0.6 seconds in my computer and that is way too long for my full program ( I do this many times).
Thanks again,
Felipe
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