Dear colleagues:
in an attempt to code the generation of random deviates for a user-defined
probability function p=[0.1176471 0.2352941 0.0588235 0.3882353 0.2
], I spent only a few minutes to write the Scilab code below and it gives me
all the solutions (frequency distribution of random numbers) that I need.
N=100;X=grand(7,N,'def');
C=[];for j=1:7;Count(1:5)=0;for k=1:N;i=1;while
X(j,k)>P(i);i=i+1;end;Count(i)=Count(i)+1;end;C=[C Count];end;
and one typical sample run yields this
C =
15. 9. 6. 12. 8. 12. 10.
20. 26. 38. 20. 23. 26. 24.
6. 7. 4. 7. 5. 10. 4.
38. 39. 32. 37. 48. 30. 39.
21. 19. 20. 24. 16. 22. 23.
However, the for and while loops will be terribly inefficient and this is not
good for large scale Monte-Carlo simulations.
Is there a way to do it with Matrix Operations?
Best greetings
Heinz
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