Struct Beta 

pub struct Beta<T> { /* private fields */ }

Beta distribution with shape parameters α and β on [0, 1].

f(x) = x^{α−1} (1−x)^{β−1} / B(α, β) for 0 ≤ x ≤ 1.

Example

use numeris::stats::{Beta, ContinuousDistribution};

let b = Beta::new(2.0_f64, 5.0).unwrap();
assert!((b.mean() - 2.0/7.0).abs() < 1e-14);

Implementations

impl<T: FloatScalar> Beta<T>

pub fn new(alpha: T, beta: T) -> Result<Self, StatsError>

Create a Beta distribution with shape parameters alpha and beta. Requires both > 0.

impl<T: FloatScalar> Beta<T>

pub fn sample(&self, rng: &mut Rng) -> T

Draw a random sample from this distribution.

Samples X ~ Gamma(alpha, 1), Y ~ Gamma(beta, 1), returns X / (X + Y).

pub fn sample_array<const K: usize>(&self, rng: &mut Rng) -> [T; K]

Fill a fixed-size array with independent samples.

Trait Implementations

impl<T: Clone> Clone for Beta<T>

fn clone(&self) -> Beta<T>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<T: FloatScalar> ContinuousDistribution<T> for Beta<T>

fn pdf(&self, x: T) -> T

Probability density function.

fn ln_pdf(&self, x: T) -> T

Natural log of the probability density function.

fn cdf(&self, x: T) -> T

Cumulative distribution function P(X ≤ x).

fn quantile(&self, p: T) -> T

Quantile function (inverse CDF). Returns x such that P(X ≤ x) = p.

fn mean(&self) -> T

Expected value E[X].

fn variance(&self) -> T

Variance Var(X).

impl<T: Debug> Debug for Beta<T>

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

Formats the value using the given formatter.

impl<T: Copy> Copy for Beta<T>