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The chi-squared distribution arises in statistics. If Y_i are n independent Gaussian random variates with unit variance then the sum-of-squares,
X_i = \sum_i Y_i^2
has a chi-squared distribution with n degrees of freedom.
This function returns a random variate from the chi-squared distribution with nu degrees of freedom. The distribution function is,
p(x) dx = {1 \over 2 \Gamma(\nu/2) } (x/2)^{\nu/2 - 1} \exp(-x/2) dx
for x >= 0.
This function computes the probability density p(x) at x for a chi-squared distribution with nu degrees of freedom, using the formula given above.
These functions compute the cumulative distribution functions P(x), Q(x) and their inverses for the chi-squared distribution with nu degrees of freedom.