How to simulate Chi-squared distribution

AI Thread Summary
Algorithms for simulating a Chi-squared distribution are discussed, particularly for cases with a very large number of degrees of freedom (DOF), such as 10^8. The Central Limit Theorem indicates that as the DOF increases, the Chi-squared distribution approximates a normal distribution, becoming highly accurate for DOF greater than 50. This approximation is expected to be nearly perfect for extremely high DOF like 10^8. Resources for further information include a specific math website and a Google search for "chi squared distribution simulation." Understanding these principles is crucial for accurate simulations in statistical analysis.
nenyan
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Is there any algorithm to simulate Chi-squared distribution?
Here, the degrees of freedom is very large. It may be 10^8.
 
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The Central Limit Theorem says that chi-squared approaches a normal distribution when the number of DOF's k becomes large. A normal distribution is an excellent approximation for k>50 in most cases, so it should be near perfect for k=10^8.
 
marcusl said:
The Central Limit Theorem says that chi-squared approaches a normal distribution when the number of DOF's k becomes large. A normal distribution is an excellent approximation for k>50 in most cases, so it should be near perfect for k=10^8.

Yes. Thank you very much.
 
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