Struct StudentT

pub struct StudentT<F>
where
    F: Float,
    StandardNormal: Distribution<F>,
    Exp1: Distribution<F>,
    Open01: Distribution<F>,
{ /* private fields */ }

The Student t-distribution t(ν).

The t-distribution is a continuous probability distribution parameterized by degrees of freedom ν (nu), which arises when estimating the mean of a normally-distributed population in situations where the sample size is small and the population’s standard deviation is unknown. It is widely used in hypothesis testing.

For ν = 1, this is equivalent to the standard Cauchy distribution, and as ν diverges to infinity, t(ν) converges to StandardNormal.

Plot

The plot shows the t-distribution with various degrees of freedom.

Example

use rand_distr::{StudentT, Distribution};

let t = StudentT::new(11.0).unwrap();
let v = t.sample(&mut rand::rng());
println!("{} is from a t(11) distribution", v)

Implementations

impl<F> StudentT<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

pub fn new(nu: F) -> Result<StudentT<F>, Error>

Create a new Student t-distribution with ν (nu) degrees of freedom.

Trait Implementations

impl<F> Clone for StudentT<F>

where F: Clone + Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F> Debug for StudentT<F>

where F: Debug + Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

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

Formats the value using the given formatter.

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

where F: Float + Deserialize<'de>, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

fn deserialize<__D>( __deserializer: __D, ) -> Result<StudentT<F>, <__D as Deserializer<'de>>::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<F> Distribution<F> for StudentT<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: 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 StudentT<F>

where F: PartialEq + Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

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

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

const 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 StudentT<F>

where F: Float + Serialize, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl<F> Copy for StudentT<F>

where F: Copy + Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

impl<F> StructuralPartialEq for StudentT<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,