Trait rv::traits::InverseCdf[−][src]

pub trait InverseCdf<X>: Rv<X> + Support<X> {
    fn invcdf(&self, p: f64) -> X;

    fn quantile(&self, p: f64) -> X { ... }

    fn interval(&self, p: f64) -> (X, X) { ... }
}

Has an inverse-CDF / quantile function

Required methods

fn invcdf(&self, p: f64) -> X[src]

The value of the x at the given probability in the CDF

Example

The CDF identity: p = CDF(x) => x = CDF-1(p)

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

let g = Gaussian::standard();

let x: f64 = 1.2;
let p: f64 = g.cdf(&x);
let y: f64 = g.invcdf(p);

// x and y should be about the same
assert!((x - y).abs() < 1E-12);
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Provided methods

fn quantile(&self, p: f64) -> X[src]

Alias for invcdf

fn interval(&self, p: f64) -> (X, X)[src]

Interval containing p proportion for the probability

Example

Confidence interval

use rv::dist::Gaussian;
use rv::traits::InverseCdf;

let g = Gaussian::new(100.0, 15.0).unwrap();
let ci: (f64, f64) = g.interval(0.68268949213708585);  // one stddev
assert!( (ci.0 - 85.0).abs() < 1E-12);
assert!( (ci.1 - 115.0).abs() < 1E-12);
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Implementors

impl InverseCdf<f32> for Cauchy[src]

impl InverseCdf<f32> for Exponential[src]

impl InverseCdf<f32> for Gaussian[src]

impl InverseCdf<f32> for KsTwoAsymptotic[src]

impl InverseCdf<f32> for Kumaraswamy[src]

impl InverseCdf<f32> for LogNormal[src]

impl InverseCdf<f32> for Uniform[src]

impl InverseCdf<f64> for Cauchy[src]

impl InverseCdf<f64> for Exponential[src]

impl InverseCdf<f64> for Gaussian[src]

impl InverseCdf<f64> for KsTwoAsymptotic[src]

impl InverseCdf<f64> for Kumaraswamy[src]

impl InverseCdf<f64> for LogNormal[src]

impl InverseCdf<f64> for Uniform[src]

impl<Fx, X> InverseCdf<X> for Fx where
    Fx: Deref,
    Fx::Target: InverseCdf<X>, [src]

impl<X, T> InverseCdf<X> for DiscreteUniform<T> where
    X: Integer + From<T> + FromPrimitive,
    T: DuParam + SampleUniform + ToPrimitive, [src]

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