Struct rand_distr::Gamma

pub struct Gamma<N> { /* fields omitted */ }

The Gamma distribution Gamma(shape, scale) distribution.

The density function of this distribution is

f(x) =  x^(k - 1) * exp(-x / θ) / (Γ(k) * θ^k)

where Γ is the Gamma function, k is the shape and θ is the scale and both k and θ are strictly positive.

The algorithm used is that described by Marsaglia & Tsang 2000¹, falling back to directly sampling from an Exponential for shape == 1, and using the boosting technique described in that paper for shape < 1.

Example

use rand_distr::{Distribution, Gamma};

let gamma = Gamma::new(2.0, 5.0).unwrap();
let v = gamma.sample(&mut rand::thread_rng());
println!("{} is from a Gamma(2, 5) distribution", v);

---

1. George Marsaglia and Wai Wan Tsang. 2000. "A Simple Method for Generating Gamma Variables" ACM Trans. Math. Softw. 26, 3 (September 2000), 363-372. DOI:10.1145/358407.358414 ↩

Methods

impl<N: Float> Gamma<N> where
    StandardNormal: Distribution<N>,
    Exp1: Distribution<N>,
    Open01: Distribution<N>, 

pub fn new(shape: N, scale: N) -> Result<Gamma<N>, Error>

Construct an object representing the Gamma(shape, scale) distribution.

Trait Implementations

impl<N: Copy> Copy for Gamma<N>

impl<N: Clone> Clone for Gamma<N>

fn clone(&self) -> Gamma<N>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<N: Debug> Debug for Gamma<N>

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl<N: Float> Distribution<N> for Gamma<N> where
    StandardNormal: Distribution<N>,
    Exp1: Distribution<N>,
    Open01: Distribution<N>, 

fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N

Generate a random value of T, using rng as the source of randomness.

Important traits for DistIter<D, R, T>

impl<D, R, T> Iterator for DistIter<D, R, T> where
    D: Distribution<T>,
    R: Rng,  type Item = T;

fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> where
    R: Rng, 

Create an iterator that generates random values of T, using rng as the source of randomness.