úÎQ³M ,      !"#$%&'()*+ experimental%Patrick Perry <patperry@stanford.edu>",-./0123456789:;HAllocate a new random number generator of the given type and initialize  it with the default seed. )Seed the generator with the given value. *Returns a value uniform in [rngMin, rngMax] !Returns a value uniform on [0,1) !Returns a value uniform on (0,1) $Returns an integer uniform on [0,n-1]. n must be greater than 0. Get the name of the generator. 5Get the largest value that the generator can return. 6Get the smallest value that the generator can return. /Get the size of the generator state, in bytes. Get the generator state. ASet the generator state. The input array should have size equal  to getSize: of the generator; otherwise, strange things will happen. copyRNG dst src6 copies the state from one generator to another. The + two generators must have the same type. HAllocate a new random number generator that is an exact copy of another  generator    experimental%Patrick Perry <patperry@stanford.edu>9<=>?@ABCDEFGHIJKLMNOPQRSTUgaussianPdf x sigma- computes the probabililty density p(x) for # a Gaussian distribution with mean 0 and standard deviation sigma. gaussianP x sigma9 computes the cumulative distribution function P(x) for # a Gaussian distribution with mean 0 and standard deviation sigma. gaussianQ x sigma9 computes the cumulative distribution function Q(x) for # a Gaussian distribution with mean 0 and standard deviation sigma. gaussianPInv p sigma6 computes the inverse of the cumulative distribution / function of a Gaussian distribution with mean 0 and standard deviation  sigma . It returns x such that P(x) = p. gaussianPInv q sigma6 computes the inverse of the cumulative distribution / function of a Gaussian distribution with mean 0 and standard deviation  sigma . It returns x such that Q(x) = q. getGaussian r sigma) gets a normal random variable with mean  0 and standard deviation sigma. & This uses the Box-Mueller algorithm. getGaussianZiggurat r sigma) gets a normal random variable with mean  0 and standard deviation sigma. 3 This uses the Marsaglia-Tsang ziggurat algorithm. getGaussianRatioMethod r sigma) gets a normal random variable with mean  0 and standard deviation sigma. 4 This uses the Kinderman-Monahan-Leva ratio method. VugaussianPdf x- computes the probabililty density p(x) for # a Gaussian distribution with mean 0 and standard deviation 1.  ugaussianP x9 computes the cumulative distribution function P(x) for # a Gaussian distribution with mean 0 and standard deviation 1.  ugaussianQ x9 computes the cumulative distribution function Q(x) for # a Gaussian distribution with mean 0 and standard deviation 1. ugaussianPInv p6 computes the inverse of the cumulative distribution / function of a Gaussian distribution with mean 0 and standard deviation  1 . It returns x such that P(x) = p. ugaussianPInv q6 computes the inverse of the cumulative distribution / function of a Gaussian distribution with mean 0 and standard deviation  1 . It returns x such that Q(x) = q. getUGaussian r) gets a normal random variable with mean  0 and standard deviation 1. & This uses the Box-Mueller algorithm. getUGaussianZiggurat r) gets a normal random variable with mean  0 and standard deviation 1. 3 This uses the Marsaglia-Tsang ziggurat algorithm. !getUGaussianRatioMethod r) gets a normal random variable with mean  0 and standard deviation 1. 4 This uses the Kinderman-Monahan-Leva ratio method. W" flatPdf x a b" computes the probability density p(x) at x for  a uniform distribution from a to b. # flatP x a b/ computes the cumulative distribution function P(x). $ flatQ x a b/ computes the cumulative distribution function Q(x). %flatPInv p a b5 computes the inverse of the cumulative distribution  and returns x so that function P(x) = p. &flatQInv q a b5 computes the inverse of the cumulative distribution  and returns x so that function Q(x) = q. ' getFlat r a b" gets a value uniformly chosen in [a,b). (poissonPdf k mu# evaluates the probability density p(k) at k for " a Poisson distribution with mean mu. ) poissonP k mu0 evaluates the cumulative distribution function P(k)  at k& for a Poisson distribution with mean mu. * poissonQ k mu0 evaluates the cumulative distribution function Q(k)  at k& for a Poisson distribution with mean mu. +getPoisson r mu* gets a poisson random variable with mean mu. XYZ !"#$%&'()*+ !"#$%&'()*+ !"#$%&'()*+ experimental%Patrick Perry <patperry@stanford.edu>  [      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_gsl-random-0.2GSL.Random.GenGSL.Random.DistGSL.Random.Gen.InternalRNGTypeRNGMkRNGnewRNGsetSeed getSample getUniform getUniformPos getUniformIntgetNamegetMaxgetMingetSizegetStatesetStatecopyRNGcloneRNGmt19937 gaussianPdf gaussianP gaussianQ gaussianPInv gaussianQInv getGaussiangetGaussianZigguratgetGaussianRatioMethod ugaussianPdf ugaussianP ugaussianQ ugaussianPInv ugaussianQInv getUGaussiangetUGaussianZigguratgetUGaussianRatioMethodflatPdfflatPflatQflatPInvflatQInvgetFlat poissonPdfpoissonPpoissonQ getPoissongsl_rng_mt19937 gsl_rng_clonegsl_rng_memcpy gsl_rng_state gsl_rng_size gsl_rng_min gsl_rng_max gsl_rng_namegsl_rng_uniform_intgsl_rng_uniform_posgsl_rng_uniform gsl_rng_get gsl_rng_setp_gsl_rng_free gsl_rng_alloc MkRNGTypegsl_ran_poissongsl_cdf_poisson_Qgsl_cdf_poisson_Pgsl_ran_poisson_pdf gsl_ran_flatgsl_cdf_flat_Qinvgsl_cdf_flat_Pinvgsl_cdf_flat_Qgsl_cdf_flat_Pgsl_ran_flat_pdfgsl_ran_ugaussian_ratio_methodgsl_ran_ugaussian_zigguratgsl_ran_ugaussiangsl_cdf_ugaussian_Qinvgsl_cdf_ugaussian_Pinvgsl_cdf_ugaussian_Qgsl_cdf_ugaussian_Pgsl_ran_ugaussian_pdfgsl_ran_gaussian_ratio_methodgsl_ran_gaussian_zigguratgsl_ran_gaussiangsl_cdf_gaussian_Qinvgsl_cdf_gaussian_Pinvgsl_cdf_gaussian_Qgsl_cdf_gaussian_Pgsl_ran_gaussian_pdfgetGaussianHelpgetUGaussianHelp liftDouble liftDouble2 liftDouble3