Merge pull request #9 from stdlib-js/feature/random

Update distribution PRNGs

Author: Planeshifter

docs/references/bib.bib

@@ -251,7 +251,7 @@ @article{ahrens:1974
 	year = {1974},
 }
 
-@article{hormann:1993,
+@article{hormann:1993a,
 	abstract = {The transformed rejection method, a combination of inversion and rejection, which can be applied to various continuous distributions, is well suited to generate binomial random variates as well. The resulting algorithms are simple and fast, and need only a short set-up. Among the many possible variants two algorithms are described and tested: BTRS a short but nevertheless fast rejection algorithm and BTRD which is more complicated as the idea of decomposition is utilized. For BTRD the average number of uniforms required to return one binomial deviate less between 2.5 and 1.4 which is considerably lower than for any of the known uniformly fast algorithms. Timings for a C-implementation show that for the case that the parameters of the binomial distribution vary from call to call BTRD is faster than the current state of the art algorithms. Depending on the computer, the speed of the uniform generator used and the binomial parameters the savings are between 5 and 40 percent.},
 	author = {Wolfgang H\"{o}rmann},
 	doi = {10.1080/00949659308811496},
@@ -534,7 +534,7 @@ @book{knuth:1997
 	year = {1997},
 }
 
-@article{hormann:1993,
+@article{hormann:1993b,
 	abstract = {The transformed rejection method, a combination of the inversion and the rejection method, which is used to generate non-uniform random numbers from a variety of continuous distributions can be applied to discrete distributions as well. For the Poisson distribution a short and simple algorithm is obtained which is well suited for large values of the Poisson parameter \\(\mu\\), even when \\(\mu\\) may vary from call to call. The average number of uniform deviates required is lower than for any of the known uniformly fast algorithms. Timings for a C implementation show that the algorithm needs only half of the code but is - for \\(\mu\\) not too small - at least as fast as the current state-of-the-art algorithms.},
 	author = {W. H\"{o}rmann},
 	doi = {10.1016/0167-6687(93)90997-4},