Struct rand_distr::NormalInverseGaussian

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

The normal-inverse Gaussian distribution

Implementations

impl<F> NormalInverseGaussian<F>where
    F: Float,
    StandardNormal: Distribution<F>,
    Standard: 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> Clone for NormalInverseGaussian<F>where
    F: Float,
    StandardNormal: Distribution<F>,
    Standard: Distribution<F>,

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for NormalInverseGaussian<F>where
    F: Float,
    StandardNormal: Distribution<F>,
    Standard: Distribution<F>,

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

Formats the value using the given formatter.

impl<F> Distribution<F> for NormalInverseGaussian<F>where
    F: Float,
    StandardNormal: Distribution<F>,
    Standard: Distribution<F>,

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

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,

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

impl<F: Copy> Copy for NormalInverseGaussian<F>where
    F: Float,
    StandardNormal: Distribution<F>,
    Standard: Distribution<F>,