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The noncentral chi-squared distribution is a generalization of the
-__chi_squared_distrib. If X[sub i] are [nu] independent, normally
-distributed random variables with means [mu][sub i] and variances
-[sigma][sub i][super 2], then the random variable
+__chi_squared_distrib. If ['X[sub i]] are /[nu]/ independent, normally
+distributed random variables with means /[mu][sub i]/ and variances
+['[sigma][sub i][super 2]], then the random variable
[equation nc_chi_squ_ref1]
is distributed according to the noncentral chi-squared distribution.
The noncentral chi-squared distribution has two parameters:
-[nu] which specifies the number of degrees of freedom
-(i.e. the number of X[sub i]), and [lambda] which is related to the
-mean of the random variables X[sub i] by:
+/[nu]/ which specifies the number of degrees of freedom
+(i.e. the number of ['X[sub i])], and [lambda] which is related to the
+mean of the random variables ['X[sub i]] by:
[equation nc_chi_squ_ref2]
[equation nc_chi_squ_ref5]
-Where P[sub a](x) is the incomplete gamma function.
+Where ['P[sub a](x)] is the incomplete gamma function.
The method starts at the [lambda]th term, which is where the Poisson weighting
function achieves its maximum value, although this is not necessarily