úÎ0O-a%      !"#$&(c) 2015-2017 Jared Tobin, Marco ZoccaMIT>Jared Tobin <jared@jtobin.ca>, Marco Zocca <zocca.marco gmail>unstableghcNoneFT,@A probability distribution characterized by a sampling function.sample uniform gen0.4208881170464097Sample from a model n times.samples 2 uniform gen'[0.6738707766845254,0.9730405951541817]%The uniform distribution over a type.sample uniform gen :: IO Double0.29308497534914946sample uniform gen :: IO BoolFalse4The uniform distribution over the provided interval.sample (uniformR (0, 1)) gen0.44984153252922365"The discrete uniform distribution.$sample (discreteUniform [0..10]) gen69sample (discreteUniform "abcdefghijklmnopqrstuvwxyz") gen'a'WThe standard normal or Gaussian distribution (with mean 0 and standard deviation 1).TThe normal or Gaussian distribution with a specified mean and standard deviation. GThe log-normal distribution with specified mean and standard deviation. :The exponential distribution with provided rate parameter. EThe Laplace distribution with provided location and scale parameters. BThe Weibull distribution with provided shape and scale parameters. DThe gamma distribution with shape parameter a and scale parameter b.—This is the parameterization used more traditionally in frequentist statistics. It has the following corresponding probability density function:f(x; a, b) = 1 ) (Gamma(a) * b ^ a) x ^ (a - 1) e ^ (- x  b)The inverse-gamma distribution.<The Normal-Gamma distribution of parameters mu, lambda, a, bThe chi-square distribution.The beta distribution.-The Pareto distribution with specified index a and minimum xmin parameters.Both a and xmin must be positive.The Dirichlet distribution.4The symmetric Dirichlet distribution of dimension n.The Bernoulli distribution.The binomial distribution.(The negative binomial distribution with n( trials each with "success" probability p. Example X.1.5 in [1].Note: n must be larger than 1 and p included between 0 and 1.The multinomial distribution.Student's t distribution.tAn isotropic or spherical Gaussian distribution with specified mean vector and scalar standard deviation parameter.The Poisson distribution.IA categorical distribution defined by the supplied list of probabilities.fThe Zipf-Mandelbrot distribution, generated with the rejection sampling algorithm X.6.1 shown in [1].¦The parameter should be positive, but values close to 1 should be avoided as they are very computationally intensive. The following code illustrates this behaviour.samples 10 (zipf 1.1) gen=[11315371987423520,2746946,653,609,2,13,85,4,256184577853,50]samples 10 (zipf 1.5) gen[19,3,3,1,1,2,1,191,2,1].%&'()*+,-./01234   5      !"#$%&'(&')&'*&'+&',&'-&'.&'/&'0&'1&'2&'3&'4&'5&'6&'78,mwc-probability-2.0.2-GHsskvaxcTJ45gSPtjYToYSystem.Random.MWC.ProbabilityProbsamplesamplesuniformuniformRdiscreteUniformstandardNormalnormal logNormal exponentiallaplaceweibullgamma inverseGamma normalGamma chiSquarebetapareto dirichletsymmetricDirichlet bernoullibinomialnegativeBinomial multinomialstudent isoNormalpoisson categoricalzipf$fPrimMonadProb $fMonadIOProb$fMonadTransProb $fNumProb $fMonadProb$fApplicativeProb $fFunctorProb*mwc-random-0.13.6.0-FVAd7inlLEjCHUPScUTDLtSystem.Random.MWC uniformVectorcreateSystemRandomwithSystemRandomrestoresavetoSeed initializecreateasGenSTasGenIOVariateGenGenIOGenSTSeedfromSeed