[−][src]Trait mathru::statistics::distrib::Discrete

pub trait Discrete<T, A, B> {
    pub fn pmf(&self, x: A) -> T;

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

    pub fn mean(&self) -> T;

    pub fn variance(&self) -> T;
}

Discrete distribution

Required methods

pub fn pmf(&self, x: A) -> T[src]

Probability mass function

Arguments

*x:

pub fn cdf(&self, x: B) -> T[src]

Cumulative distribution function

Arguments

  • x:

pub fn mean(&self) -> T[src]

Mean

pub fn variance(&self) -> T[src]

Variance

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Implementors

impl<T> Discrete<T, u8, T> for Bernoulli<T> where
    T: Real, [src]

pub fn pmf<'a>(&'a self, x: u8) -> T[src]

Probability mass function of the Bernoulli distribution

Arguments

  • x Random variable x ∈ {0, 1}

Panics

if x ∉ {0, 1}

Example

use mathru::statistics::distrib::{Bernoulli, Discrete};

let distrib: Bernoulli<f64> = Bernoulli::new(0.2);
let x: u8 = 0;
let p: f64 = distrib.pmf(x);

pub fn cdf<'a>(&'a self, x: T) -> T[src]

Cumulative distribution function of the Bernoulli distribution

Arguments

  • x Random variable x ∈ {0, 1}

Example

use mathru::statistics::distrib::{Bernoulli, Discrete};

let distrib: Bernoulli<f64> = Bernoulli::new(0.2);
let x: f64 = 0.4;
let p: f64 = distrib.cdf(x);

pub fn mean<'a>(&'a self) -> T[src]

Expected value

Example

use mathru::statistics::distrib::{Bernoulli, Discrete};

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

pub fn variance<'a>(&'a self) -> T[src]

Variance

Example

use mathru::statistics::distrib::{Bernoulli, Discrete};

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

impl<T> Discrete<T, u32, u32> for Poisson<T> where
    T: Real + Gamma, [src]

pub fn pmf<'a>(&'a self, x: u32) -> T[src]

Probability mass function

Arguments

  • x Random variable x ∈ ℕ

Example

use mathru::statistics::distrib::{Discrete, Poisson};

let distrib: Poisson<f64> = Poisson::new(&0.2);
let x: u32 = 5;
let p: f64 = distrib.pmf(x);

pub fn cdf<'a>(&'a self, x: u32) -> T[src]

Cumulative distribution function of the Bernoulli distribution

Arguments

  • x Random variable x ∈ ℕ

Example

use mathru::statistics::distrib::{Discrete, Poisson};

let distrib: Poisson<f64> = Poisson::new(&0.2);
let x: u32 = 4;
let p: f64 = distrib.cdf(x);

pub fn mean<'a>(&'a self) -> T[src]

Expected value

Example

use mathru::statistics::distrib::{Discrete, Poisson};

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

pub fn variance<'a>(&'a self) -> T[src]

Variance

Example

use mathru::statistics::distrib::{Discrete, Poisson};

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

impl<T> Discrete<T, u32, T> for Binomial<T> where
    T: Real, [src]

pub fn pmf<'a>(&'a self, x: u32) -> T[src]

Probability mass function

Arguments

  • x Random variable x &isin ࡃ

Example

use mathru::statistics::distrib::{Binomial, Discrete};

let distrib: Binomial<f64> = Binomial::new(5, 0.3);
let x: u32 = 0;
let p: f64 = distrib.pmf(x);

pub fn cdf<'a>(&'a self, x: T) -> T[src]

Cumulative distribution function

Arguments

  • x Random variable

Example

use mathru::statistics::distrib::{Binomial, Discrete};

let distrib: Binomial<f64> = Binomial::new(5, 0.3);
let x: f64 = 0.4;
let p: f64 = distrib.cdf(x);

pub fn mean<'a>(&'a self) -> T[src]

Expected value

Example

use mathru::statistics::distrib::{Binomial, Discrete};

let distrib: Binomial<f64> = Binomial::new(5, 0.3);
let mean: f64 = distrib.mean();

pub fn variance<'a>(&'a self) -> T[src]

Variance

Example

use mathru::statistics::distrib::{Binomial, Discrete};

let distrib: Binomial<f64> = Binomial::new(5, 0.3);
let var: f64 = distrib.variance();
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