Struct Beta 

pub struct Beta {
    pub alpha: f64,
    pub beta: f64,
}

Beta distribution Beta(α, β).

Examples

use rs_stats::distributions::beta::Beta;
use rs_stats::distributions::traits::Distribution;

let b = Beta::new(2.0, 5.0).unwrap();
assert!((b.mean() - 2.0 / 7.0).abs() < 1e-10);

Fields

alpha: f64

Shape parameter α > 0

beta: f64

Shape parameter β > 0

Implementations

impl Beta

pub fn new(alpha: f64, beta: f64) -> StatsResult<Self>

Creates a Beta(α, β) distribution. Both parameters must be positive.

pub fn fit(data: &[f64]) -> StatsResult<Self>

MLE via method of moments from data in [0, 1].

Requires all data in (0, 1). Estimates α and β from sample mean and variance.

Trait Implementations

impl Clone for Beta

fn clone(&self) -> Beta

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Beta

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

Formats the value using the given formatter.

impl Distribution for Beta

fn name(&self) -> &str

Human-readable distribution name, e.g. "Normal".

fn num_params(&self) -> usize

Number of free parameters (used when computing AIC / BIC).

fn pdf(&self, x: f64) -> StatsResult<f64>

Probability density function f(x).

fn logpdf(&self, x: f64) -> StatsResult<f64>

Natural logarithm of the PDF: ln f(x).

fn cdf(&self, x: f64) -> StatsResult<f64>

Cumulative distribution function F(x) = P(X ≤ x).

fn inverse_cdf(&self, p: f64) -> StatsResult<f64>

Quantile (inverse CDF): find x such that F(x) = p.

fn mean(&self) -> f64

Mean (expected value) μ.

fn variance(&self) -> f64

Variance σ².

fn std_dev(&self) -> f64

Standard deviation σ = √(variance).

fn log_likelihood(&self, data: &[f64]) -> StatsResult<f64>

Sum of log-likelihoods: Σ ln f(xᵢ).

fn aic(&self, data: &[f64]) -> StatsResult<f64>

Akaike Information Criterion: AIC = 2k − 2·ln(L̂).

fn bic(&self, data: &[f64]) -> StatsResult<f64>

Bayesian Information Criterion: BIC = k·ln(n) − 2·ln(L̂).

impl Copy for Beta