Struct mathru::statistics::distrib::Exponential

pub struct Exponential<T> { /* fields omitted */ }

Exponential distribution

Fore more information: https://en.wikipedia.org/wiki/Exponential_distribution

Implementations

impl<T> Exponential<T> where
    T: Real, 

pub fn new(lambda: T) -> Exponential<T>

Creates a probability distribution

Arguments

• lambda > 0.0

Panics

if lambda <= 0.0

Example

use mathru::statistics::distrib::Exponential;

let distrib: Exponential<f64> = Exponential::new(0.3);

pub fn from_data<'a>(data: &'a Vec<T>) -> Self

impl<T> Exponential<T> where
    T: Real, 

pub fn random(&self) -> T

Trait Implementations

impl<T> Continuous<T> for Exponential<T> where
    T: Real, 

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

Probability density function

Arguments

• x Random variable x ∈ &#x2115 | x > 0.0

Example

use mathru::statistics::distrib::{Continuous, Exponential};

let distrib: Exponential<f64> = Exponential::new(0.3);
let x: f64 = 5.0;
let p: f64 = distrib.pdf(x);

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

Cumulative distribution function

Arguments

• x Random variable

Example

use mathru::statistics::distrib::{Continuous, Exponential};

let distrib: Exponential<f64> = Exponential::new(0.3);
let x: f64 = 0.4;
let p: f64 = distrib.cdf(x);

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

Quantile function of inverse cdf

fn mean(&self) -> T

Expected value

Example

use mathru::statistics::distrib::{Continuous, Exponential};

let distrib: Exponential<f64> = Exponential::new(0.2);
let mean: f64 = distrib.mean();

fn variance(&self) -> T

Variance

Example

use mathru::statistics::distrib::{Continuous, Exponential};

let distrib: Exponential<f64> = Exponential::new(0.2);
let var: f64 = distrib.variance();

fn skewness(&self) -> T

fn median(&self) -> T

fn entropy(&self) -> T