Trait rv::traits::Cdf

pub trait Cdf<X>: Rv<X> {
    fn cdf(&self, x: &X) -> f64;

    fn sf(&self, x: &X) -> f64 { ... }
}

Has a cumulative distribution function (CDF)

Required methods

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

The value of the Cumulative Density Function at x

Example

The proportion of probability in (-∞, μ) in N(μ, σ) is 50%

use rv::dist::Gaussian;
use rv::traits::Cdf;

let g = Gaussian::new(1.0, 1.5).unwrap();

assert!((g.cdf(&1.0_f64) - 0.5).abs() < 1E-12);

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Provided methods

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

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Implementors

impl Cdf<f32> for Beta

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for Cauchy

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for ChiSquared

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for Exponential

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for Gamma

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for Gaussian

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for Gev

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for InvChiSquared

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for InvGamma

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for InvGaussian

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for KsTwoAsymptotic

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for Kumaraswamy

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for Laplace

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for LogNormal

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for Pareto

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for ScaledInvChiSquared

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for Uniform

fn cdf(&self, x: &f32) -> f64

impl Cdf<f32> for VonMises

fn cdf(&self, x: &f32) -> f64

impl Cdf<f64> for Beta

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

impl Cdf<f64> for Cauchy

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

impl Cdf<f64> for ChiSquared

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

impl Cdf<f64> for Empirical

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

impl Cdf<f64> for Exponential

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

impl Cdf<f64> for Gamma

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

impl Cdf<f64> for Gaussian

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

impl Cdf<f64> for Gev

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

impl Cdf<f64> for InvChiSquared

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

impl Cdf<f64> for InvGamma

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

impl Cdf<f64> for InvGaussian

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

impl Cdf<f64> for KsTwoAsymptotic

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

impl Cdf<f64> for Kumaraswamy

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

impl Cdf<f64> for Laplace

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

impl Cdf<f64> for LogNormal

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

impl Cdf<f64> for Pareto

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

impl Cdf<f64> for ScaledInvChiSquared

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

impl Cdf<f64> for Uniform

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

impl Cdf<f64> for VonMises

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

impl Cdf<i8> for BetaBinomial

fn cdf(&self, k: &i8) -> f64

impl Cdf<i8> for Binomial

fn cdf(&self, k: &i8) -> f64

impl Cdf<i16> for BetaBinomial

fn cdf(&self, k: &i16) -> f64

impl Cdf<i16> for Binomial

fn cdf(&self, k: &i16) -> f64

impl Cdf<i32> for BetaBinomial

fn cdf(&self, k: &i32) -> f64

impl Cdf<i32> for Binomial

fn cdf(&self, k: &i32) -> f64

impl Cdf<i64> for BetaBinomial

fn cdf(&self, k: &i64) -> f64

impl Cdf<i64> for Binomial

fn cdf(&self, k: &i64) -> f64

impl Cdf<u8> for BetaBinomial

fn cdf(&self, k: &u8) -> f64

impl Cdf<u8> for Binomial

fn cdf(&self, k: &u8) -> f64

impl Cdf<u8> for NegBinomial

fn cdf(&self, x: &u8) -> f64

impl Cdf<u8> for Poisson

fn cdf(&self, x: &u8) -> f64

impl Cdf<u16> for BetaBinomial

fn cdf(&self, k: &u16) -> f64

impl Cdf<u16> for Binomial

fn cdf(&self, k: &u16) -> f64

impl Cdf<u16> for NegBinomial

fn cdf(&self, x: &u16) -> f64

impl Cdf<u16> for Poisson

fn cdf(&self, x: &u16) -> f64

impl Cdf<u32> for BetaBinomial

fn cdf(&self, k: &u32) -> f64

impl Cdf<u32> for Binomial

fn cdf(&self, k: &u32) -> f64

impl Cdf<u32> for NegBinomial

fn cdf(&self, x: &u32) -> f64

impl Cdf<u32> for Poisson

fn cdf(&self, x: &u32) -> f64

impl Cdf<u64> for BetaBinomial

fn cdf(&self, k: &u64) -> f64

impl Cdf<u64> for Binomial

fn cdf(&self, k: &u64) -> f64

impl Cdf<usize> for BetaBinomial

fn cdf(&self, k: &usize) -> f64

impl Cdf<usize> for Binomial

fn cdf(&self, k: &usize) -> f64

impl<Fx, X> Cdf<X> for Fx where
    Fx: Deref,
    Fx::Target: Cdf<X>, 

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

fn sf(&self, x: &X) -> f64

impl<X> Cdf<X> for Geometric where
    X: Unsigned + Integer + FromPrimitive + ToPrimitive + Saturating + Bounded, 

fn cdf(&self, k: &X) -> f64

impl<X, Fx> Cdf<X> for Mixture<Fx> where
    Fx: Rv<X> + Cdf<X>, 

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

impl<X, T> Cdf<X> for DiscreteUniform<T> where
    X: Integer + From<T> + ToPrimitive + Copy,
    T: DuParam + SampleUniform + ToPrimitive, 

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

impl<X: Booleable> Cdf<X> for Bernoulli

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

impl<X: CategoricalDatum> Cdf<X> for Categorical

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

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