Add prng refs

kgryte · kgryte · commit c3a9e2c87d79 · 2016-10-15T11:10:10.000-07:00
diff --git a/docs/references/bib.bib b/docs/references/bib.bib
@@ -1,7 +1,7 @@
 @techreport{beebe:2002,
 	abstract = {These notes describe an implementation of an algorithm for accurate computation of expm1(x) = exp(x) − 1, one of the new elementary functions introduced in the 1999 ISO C Standard, but already available in most UNIX C implementations. A test package modeled after the Cody and Waite Elementary Function Test Package, ELEFUNT, is developed to evaluate the accuracy of implementations of expm1(x).},
 	author = {Nelson H.F. Beebe},
-	keywords = {mathematics,math,special function,exponential,exp},
+	keywords = {mathematics, math, special function, exponential, exp},
 	institution = {University of Utah},
 	title = {{Computation of expm1(x) = exp(x) − 1}},
 	url = {http://www.math.utah.edu/~beebe/reports/expm1.pdf},
@@ -199,7 +199,7 @@ @article{panneton:2005
 	abstract = {G. Marsaglia recently introduced a class of very fast xorshift random number generators, whose implementation uses three “xorshift” operations. They belong to a large family of generators based on linear recurrences modulo 2, which also includes shift-register generators, the Mersenne twister, and several others. In this article, we analyze the theoretical properties of xorshift generators, search for the best ones with respect to the equidistribution criterion, and test them empirically. We find that the vast majority of xorshift generators with only three xorshift operations, including those having good equidistribution, fail several simple statistical tests. We also discuss generators with more than three xorshifts.},
 	acmid = {1113319},
 	address = {New York, NY, USA},
-	author = {Panneton, Fran\c{c}ois and L'Ecuyer, Pierre},
+	author = {Fran\c{c}ois Panneton and Pierre L'Ecuyer},
 	doi = {10.1145/1113316.1113319},
 	issn = {1049-3301},
 	journal = {ACM Trans. Model. Comput. Simul.},
@@ -236,3 +236,123 @@ @article{hellekalek:1998
 	year = {1998},
 }
 
+@article{ahrens:1974,
+	abstract = {Accurate computer methods are evaluated which transform uniformly distributed random numbers into quantities that follow gamma, beta, Poisson, binomial and negative-binomial distributions. All algorithms are designed for variable parameters. The known convenient methods are slow when the parameters are large. Therefore new procedures are introduced which can cope efficiently with parameters of all sizes. Some algorithms require sampling from the normal distribution as an intermediate step. In the reported computer experiments the normal deviates were obtained from a recent method which is also described.},
+	author = {J.H. Ahrens and U. Dieter},
+	doi = {10.1007/BF02293108},
+	issn = {1436-5057},
+	journal = {Computing},
+	keywords = {random, prng, rng, rand, beta, computation, pseudorandom, number, generator},
+	number = {3},
+	pages = {223--246},
+	title = {{Computer methods for sampling from gamma, beta, poisson and bionomial distributions}},
+	url = {http://dx.doi.org/10.1007/BF02293108},
+	volume = {12},
+	year = {1974},
+}
+
+@article{hormann:1993,
+	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},
+	journal = {Journal of Statistical Computation and Simulation},
+	keywords = {random, rand, prng, rng, binomial, pseudorandom, number, generator},
+	number = {1-2},
+	pages = {101-110},
+	title = {{The generation of binomial random variates}},
+	url = {http://dx.doi.org/10.1080/00949659308811496},
+	volume = {46},
+	year = {1993},
+}
+
+@article{box:1958,
+	abstract = {},
+	author = {G. E. P. Box and Mervin E. Muller},
+	doi = {10.1214/aoms/1177706645},
+	issn = {00034851},
+	journal = {The Annals of Mathematical Statistics},
+	keywords = {random, prng, rng, pseudorandom, rand, normal, gaussian, number, generator},
+	month = {jun},
+	number = {2},
+	pages = {610--611},
+	publisher = {The Institute of Mathematical Statistics},
+	title = {{A Note on the Generation of Random Normal Deviates}},
+	url = {http://dx.doi.org/10.1214/aoms/1177706645},
+	volume = {29},
+	year = {1958}
+}
+
+@article{bell:1968,
+	abstract = {},
+	acmid = {363547},
+	address = {New York, NY, USA},
+	author = {James R. Bell},
+	doi = {10.1145/363397.363547},
+	issn = {0001-0782},
+	issue_date = {July 1968},
+	journal = {Communications of the ACM},
+	keywords = {frequency distribution, normal deviates, normal distribution, probability distribution, random, random number, random number generator, simulation, prng, rng},
+	month = {jul},
+	number = {7},
+	pages = {498--},
+	publisher = {ACM},
+	title = {{Algorithm 334: Normal Random Deviates}},
+	url = {http://doi.acm.org/10.1145/363397.363547},
+	volume = {11},
+	year = {1968},
+}
+
+@article{knop:1969,
+	abstract = {},
+	acmid = {362996},
+	address = {New York, NY, USA},
+	author = {R. Knop},
+	doi = {10.1145/362946.362996},
+	issn = {0001-0782},
+	issue_date = {May 1969},
+	journal = {Communications of the ACM},
+	keywords = {frequency distribution, normal deviates, normal distribution, probability distribution, random, random number, random number generator, simulation},
+	month = {may},
+	number = {5},
+	pages = {281--},
+	publisher = {ACM},
+	title = {{Remark on Algorithm 334 [G5]: Normal Random Deviates}},
+	url = {http://doi.acm.org/10.1145/362946.362996},
+	volume = {12},
+	year = {1969},
+}
+
+@article{marsaglia:1964,
+	abstract = {A normal random variable X may be generated in terms of uniform random variables \\(u_1\\), \\(u_2\\), in the following simple way: 86 percent of the time, put \\(X = 2(u_1 + u_2 + u_3 - 1.5)\\),11 percent of the time, put \\(X = 1.5(u_1 + u_2 - 1)\\), and the remaining 3 percent of the time, use a more complicated procedure so that the resulting mixture is correct. This method takes only half again as long as the very fastest methods, is much simpler, and requires very little storage space.},
+	author = {G. Marsaglia and T. A. Bray},
+	issn = {00361445},
+	journal = {SIAM Review},
+	keywords = {prng, rng, random, rand, pseudorandom, number, generator, normal, gaussian},
+	number = {3},
+	pages = {260-264},
+	publisher = {Society for Industrial and Applied Mathematics},
+	title = {{A Convenient Method for Generating Normal Variables}},
+	url = {http://www.jstor.org/stable/2027592},
+	volume = {6},
+	year = {1964},
+}
+
+@article{thomas:2007,
+	abstract = {Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number generators (GRNG). This article describes the algorithms underlying various GRNGs, compares their computational requirements, and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function.},
+	acmid = {1287622},
+	address = {New York, NY, USA},
+	articleno = {11},
+	author = {David B. Thomas and Wayne Luk and Philip H.W. Leong and John D. Villasenor},
+	doi = {10.1145/1287620.1287622},
+	issn = {0360-0300},
+	issue_date = {2007},
+	journal = {ACM Computing Surveys},
+	keywords = {gaussian, random numbers, normal, simulation, random, rand, prng, rng, pseudorandom},
+	month = {nov},
+	number = {4},
+	publisher = {ACM},
+	title = {{Gaussian Random Number Generators}},
+	url = {http://doi.acm.org/10.1145/1287620.1287622},
+	volume = {39},
+	year = {2007},
+}
