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distributions

The distributions module exposes single-sample distribution functions and a named-distribution dist_sample_n runner. For PDF/CDF/quantile functions and sampling from heavier distributions (chi-square, Student’s t, beta, gamma), see distributions_advanced.

use std::core::distributions

Each call returns a single sample.

FunctionDescription
dist_uniform(lo: number, hi: number)Uniform U(lo, hi)
dist_lognormal(mean: number, std: number)Lognormal with underlying-normal (mean, std)
dist_exponential(lambda: number)Exponential with rate lambda
dist_poisson(lambda: number)Poisson with rate lambda
use std::core::distributions
let u = distributions::dist_uniform(0.0, 1.0)
let p = distributions::dist_poisson(3.5)

distributions::dist_sample_n(name: string, params: Array<number>, n: int) -> Array<number>

Section titled “distributions::dist_sample_n(name: string, params: Array<number>, n: int) -> Array<number>”

Sample n values from a named distribution. name is one of "uniform" | "lognormal" | "exponential" | "poisson"; params matches that distribution’s argument order.

let samples = distributions::dist_sample_n("uniform", [0.0, 1.0], 1000)

The distributions_advanced module ships PDF / CDF / sampling for additional distributions, plus helpers gamma, beta_fn, and special functions.

use std::core::distributions_advanced
FamilyFunctions
Normalnormal_pdf(x, mu?, sigma?), normal_cdf(x, mu?, sigma?), normal_quantile(p, mu?, sigma?)
Chi-squarechi_square_pdf(x, k), chi_square_cdf(x, k), chi_square_sample(k)
Student’s tt_pdf(x, df), t_cdf(x, df), t_sample(df)
Betabeta_pdf(x, a, b), beta_cdf(x, a, b), beta_sample(a, b)
Gammagamma_pdf(x, k, theta?), gamma_cdf(x, k, theta?), gamma_sample(k, theta?)
Specialgamma(x), beta_fn(a, b)
use std::core::distributions_advanced
print(distributions_advanced::normal_cdf(1.96)) // ~0.975
let z = distributions_advanced::normal_quantile(0.95)
  • random — low-level PRNG primitives
  • stochastic — continuous-time processes built on distributions
  • monte_carlo — simulation runner with variance reduction