move refs to footnote

Author: HugoMVale

doc/specs/stdlib_stats_distribution_normal.md

@@ -22,7 +22,8 @@ With two arguments, the function returns a normal distributed random variate $N(
 
 With three arguments, the function returns a rank-1 array of normal distributed random variates.
 
-Note: the algorithm used for generating normal random variates is fundamentally limited to double precision.
+@note
+The algorithm used for generating exponential random variates is fundamentally limited to double precision.[^1]
 
 ### Syntax
 
@@ -64,7 +65,7 @@ The probability density function (pdf) of the single real variable normal distri
 
 $$f(x) = \frac{1}{\sigma \sqrt{2}} \exp{\left[-\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^{2}\right]}$$
 
-For a complex varible $z=(x + y i)$ with independent real $x$ and imaginary $y$ parts, the joint probability density function is the product of the the corresponding real and imaginary marginal pdfs (ref. "Probability and Random Processes with Applications to Signal Processing and Communications", 2nd ed., Scott L. Miller and Donald Childers, 2012, p.197):
+For a complex varible $z=(x + y i)$ with independent real $x$ and imaginary $y$ parts, the joint probability density function is the product of the the corresponding real and imaginary marginal pdfs:[^2]
 
 $$f(x + y \mathit{i}) = f(x) f(y) = \frac{1}{2\sigma_{x}\sigma_{y}} \exp{\left[-\frac{1}{2}\left(\left(\frac{x-\mu_x}{\sigma_{x}}\right)^{2}+\left(\frac{y-\mu_y}{\sigma_{y}}\right)^{2}\right)\right]}$$
 
@@ -108,7 +109,7 @@ Cumulative distribution function of the single real variable normal distribution
 
 $$F(x) = \frac{1}{2}\left [ 1+\text{erf}\left(\frac{x-\mu}{\sigma \sqrt{2}}\right) \right ]$$
 
-For the complex variable $z=(x + y i)$ with independent real $x$ and imaginary $y$ parts, the joint cumulative distribution function is the product of the corresponding real and imaginary marginal cdfs (ref. "Probability and Random Processes with Applications to Signal Processing and Communications", 2nd ed., Scott L. Miller and Donald Childers, 2012, p.197):
+For the complex variable $z=(x + y i)$ with independent real $x$ and imaginary $y$ parts, the joint cumulative distribution function is the product of the corresponding real and imaginary marginal cdfs:[^2]
 
 $$ F(x+y\mathit{i})=F(x)F(y)=\frac{1}{4} \
 \left[ 1+\text{erf}\left(\frac{x-\mu_x}{\sigma_x \sqrt{2}}\right) \right] \
@@ -141,3 +142,7 @@ The result is a scalar or an array, with a shape conformable to arguments, as th
 ```fortran
 {!example/stats_distribution_normal/example_norm_cdf.f90!}
 ```
+
+[^1] Marsaglia, George, and Wai Wan Tsang. "The ziggurat method for generating random variables." _Journal of statistical software_ 5 (2000): 1-7.
+
+[^2] Miller, Scott, and Donald Childers. _Probability and random processes: With applications to signal processing and communications_. Academic Press, 2012 (p. 197).