Struct InverseGaussian

pub struct InverseGaussian<F>
where
    F: Float,
    StandardNormal: Distribution<F>,
    StandardUniform: Distribution<F>,
{ /* private fields */ }

The inverse Gaussian distribution IG(μ, λ).

This is a continuous probability distribution with mean parameter μ (mu) and shape parameter λ (lambda), defined for x > 0. It is also known as the Wald distribution.

Plot

The following plot shows the inverse Gaussian distribution with various values of μ and λ.

Example

use rand_distr::{InverseGaussian, Distribution};

let inv_gauss = InverseGaussian::new(1.0, 2.0).unwrap();
let v = inv_gauss.sample(&mut rand::rng());
println!("{} is from a inverse Gaussian(1, 2) distribution", v);

Implementations

impl<F> InverseGaussian<F>

where F: Float, StandardNormal: Distribution<F>, StandardUniform: Distribution<F>,

pub fn new(mean: F, shape: F) -> Result<InverseGaussian<F>, Error>

Construct a new InverseGaussian distribution with the given mean and shape.

Trait Implementations

impl<F> Clone for InverseGaussian<F>

where F: Float + Clone, StandardNormal: Distribution<F>, StandardUniform: Distribution<F>,

fn clone(&self) -> InverseGaussian<F>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F> Debug for InverseGaussian<F>

where F: Float + Debug, StandardNormal: Distribution<F>, StandardUniform: Distribution<F>,

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

Formats the value using the given formatter.

impl<'de, F> Deserialize<'de> for InverseGaussian<F>

where F: Float + Deserialize<'de>, StandardNormal: Distribution<F>, StandardUniform: Distribution<F>,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<F> Distribution<F> for InverseGaussian<F>

where F: Float, StandardNormal: Distribution<F>, StandardUniform: Distribution<F>,

fn sample<R>(&self, rng: &mut R) -> F

where R: Rng + ?Sized,

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

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

where R: Rng, Self: Sized,

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

fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>

where F: Fn(T) -> S, Self: Sized,

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F

impl<F> PartialEq for InverseGaussian<F>

where F: Float + PartialEq, StandardNormal: Distribution<F>, StandardUniform: Distribution<F>,

fn eq(&self, other: &InverseGaussian<F>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<F> Serialize for InverseGaussian<F>

where F: Float + Serialize, StandardNormal: Distribution<F>, StandardUniform: Distribution<F>,

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl<F> Copy for InverseGaussian<F>

where F: Float + Copy, StandardNormal: Distribution<F>, StandardUniform: Distribution<F>,

impl<F> StructuralPartialEq for InverseGaussian<F>

where F: Float, StandardNormal: Distribution<F>, StandardUniform: Distribution<F>,