Hi
I join a vpm file with a model and different seed values showing that the average Bernouilli value does not converge properly towards the original frequency depending on the seed value.
Best regards.
JJ
Problem with the random uniform generator
Problem with the random uniform generator
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- test1234.vdf
- (1.43 MiB) Downloaded 188 times
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- test4444.vdf
- (1.43 MiB) Downloaded 189 times
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- test3.vpm
- (78.93 KiB) Downloaded 177 times
Re: Problem with the random uniform generator
other vdf files
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- test0.vdf
- (1.43 MiB) Downloaded 180 times
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- test4542.vdf
- (1.43 MiB) Downloaded 191 times
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- test7876.vdf
- (1.43 MiB) Downloaded 178 times
Re: Problem with the random uniform generator
I'm not convinced that there's a problem. I used the attached modified model to run a sample of 1000 realizations at once, and looked at the statistics for the cumulated "input". The expected value of the standard deviation of "input" given by the binomial distribution is sqrt(100000*0.2*(1-0.2))=126.5. In 2 tests with different seeds I got ~123 and 129. So, I think your threshold for "strangeness" is too strict, because .001*100000 < 126.5.
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Advice to posters (it really helps us to help you)
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Re: Problem with the random uniform generator
Forgot the attachment:
/*
Advice to posters (it really helps us to help you)
http://www.ventanasystems.co.uk/forum/v ... f=2&t=4391
Blog: http://blog.metasd.com
Model library: http://models.metasd.com
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*/
Advice to posters (it really helps us to help you)
http://www.ventanasystems.co.uk/forum/v ... f=2&t=4391
Blog: http://blog.metasd.com
Model library: http://models.metasd.com
Bookmarks: http://delicious.com/tomfid/SystemDynamics
*/
Re: Problem with the random uniform generator
Hi Tom
You are right. In fact if one applies Tchebychev inequailty that sets the maximum probability that the gap is more than the epsilon, one finds 1.6, whch means that there is no limitation to the likelyhood of that event happening. I have tested that the Techbychev inequatily that proves that the precision of the estimation of the average increases with the number of draws, works for any seed, any epsilon and any number of draws. See the model joined.
In fact I have a model where I have about 8000 draws and a frequency of 0.2 and with a seed of 4000, I get an estimation of the frequency a little more than 0.21. In that case with the Tchebychev inequality there is a 0.16 maximum probability of this event happening.
Thank you for your help.
Best regards.
JJ
You are right. In fact if one applies Tchebychev inequailty that sets the maximum probability that the gap is more than the epsilon, one finds 1.6, whch means that there is no limitation to the likelyhood of that event happening. I have tested that the Techbychev inequatily that proves that the precision of the estimation of the average increases with the number of draws, works for any seed, any epsilon and any number of draws. See the model joined.
In fact I have a model where I have about 8000 draws and a frequency of 0.2 and with a seed of 4000, I get an estimation of the frequency a little more than 0.21. In that case with the Tchebychev inequality there is a 0.16 maximum probability of this event happening.
Thank you for your help.
Best regards.
JJ
- Attachments
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- test3_sub_2_bis.mdl
- (4.85 KiB) Downloaded 188 times