Trait rv::traits::Cdf

pub trait Cdf<X>: HasDensity<X> {
    // Required method
    fn cdf(&self, x: &X) -> f64;

    // Provided method
    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);

Provided Methods

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

Survival function, 1 - CDF(x)

Implementors

impl Cdf<f32> for Beta

impl Cdf<f32> for Cauchy

impl Cdf<f32> for ChiSquared

impl Cdf<f32> for Exponential

impl Cdf<f32> for Gamma

impl Cdf<f32> for Gaussian

impl Cdf<f32> for Gev

impl Cdf<f32> for InvChiSquared

impl Cdf<f32> for InvGamma

impl Cdf<f32> for InvGaussian

impl Cdf<f32> for KsTwoAsymptotic

impl Cdf<f32> for Kumaraswamy

impl Cdf<f32> for Laplace

impl Cdf<f32> for LogNormal

impl Cdf<f32> for Pareto

impl Cdf<f32> for ScaledInvChiSquared

impl Cdf<f32> for Uniform

impl Cdf<f32> for UnitPowerLaw

impl Cdf<f32> for VonMises

impl Cdf<f64> for Beta

impl Cdf<f64> for Cauchy

impl Cdf<f64> for ChiSquared

impl Cdf<f64> for Empirical

impl Cdf<f64> for Exponential

impl Cdf<f64> for Gamma

impl Cdf<f64> for Gaussian

impl Cdf<f64> for Gev

impl Cdf<f64> for InvChiSquared

impl Cdf<f64> for InvGamma

impl Cdf<f64> for InvGaussian

impl Cdf<f64> for KsTwoAsymptotic

impl Cdf<f64> for Kumaraswamy

impl Cdf<f64> for Laplace

impl Cdf<f64> for LogNormal

impl Cdf<f64> for Pareto

impl Cdf<f64> for ScaledInvChiSquared

impl Cdf<f64> for Uniform

impl Cdf<f64> for UnitPowerLaw

impl Cdf<f64> for VonMises

impl Cdf<i8> for BetaBinomial

impl Cdf<i8> for Binomial

impl Cdf<i16> for BetaBinomial

impl Cdf<i16> for Binomial

impl Cdf<i32> for BetaBinomial

impl Cdf<i32> for Binomial

impl Cdf<i64> for BetaBinomial

impl Cdf<i64> for Binomial

impl Cdf<u8> for BetaBinomial

impl Cdf<u8> for Binomial

impl Cdf<u8> for NegBinomial

impl Cdf<u8> for Poisson

impl Cdf<u16> for BetaBinomial

impl Cdf<u16> for Binomial

impl Cdf<u16> for NegBinomial

impl Cdf<u16> for Poisson

impl Cdf<u32> for BetaBinomial

impl Cdf<u32> for Binomial

impl Cdf<u32> for NegBinomial

impl Cdf<u32> for Poisson

impl Cdf<u64> for BetaBinomial

impl Cdf<u64> for Binomial

impl Cdf<usize> for BetaBinomial

impl Cdf<usize> for Binomial

impl Cdf<usize> for Poisson

impl Cdf<usize> for StickBreakingDiscrete

Implementation of the Cdf trait for StickBreakingDiscrete.

impl<X> Cdf<X> for Geometric

where X: Unsigned + Integer + FromPrimitive + ToPrimitive + Saturating + Bounded,

impl<X, Fx> Cdf<X> for Mixture<Fx>

where Fx: Rv<X> + Cdf<X>,

impl<X, T> Cdf<X> for DiscreteUniform<T>

where X: Integer + From<T> + ToPrimitive + Copy, T: DuParam + SampleUniform + ToPrimitive,

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

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