Struct NormalInverseGaussian 

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

The normal-inverse Gaussian distribution NIG(α, β).

This is a continuous probability distribution with two parameters, α (alpha) and β (beta), defined in (-∞, ∞). It is also known as the normal-Wald distribution.

Plot

The following plot shows the normal-inverse Gaussian distribution with various values of α and β.

Example

use rand_distr::{NormalInverseGaussian, Distribution};

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

Implementations

impl<F> NormalInverseGaussian<F>

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

pub fn new(alpha: F, beta: F) -> Result<NormalInverseGaussian<F>, Error>

Construct a new NormalInverseGaussian distribution with the given alpha (tail heaviness) and beta (asymmetry) parameters.

Trait Implementations

impl<F> Clone for NormalInverseGaussian<F>

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

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F> Debug for NormalInverseGaussian<F>

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

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

Formats the value using the given formatter.

impl<F> Distribution<F> for NormalInverseGaussian<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) -> Iter<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) -> Map<Self, F, T, S>

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

Map sampled values to type S

impl<F> PartialEq for NormalInverseGaussian<F>

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

fn eq(&self, other: &NormalInverseGaussian<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> Copy for NormalInverseGaussian<F>

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

impl<F> StructuralPartialEq for NormalInverseGaussian<F>

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