Trait rv::traits::Cdf [−][src]
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
Implementors
impl Cdf<bool> for Bernoulli
impl Cdf<u8> for Bernoulli
impl Cdf<u16> for Bernoulli
impl Cdf<u32> for Bernoulli
impl Cdf<u64> for Bernoulli
impl Cdf<usize> for Bernoulli
impl Cdf<i8> for Bernoulli
impl Cdf<i16> for Bernoulli
impl Cdf<i32> for Bernoulli
impl Cdf<i64> for Bernoulli
impl Cdf<isize> for Bernoulli
impl Cdf<f64> for Cauchy
impl Cdf<f32> for Cauchy
impl Cdf<f64> for ChiSquared
impl Cdf<f32> for ChiSquared
impl Cdf<f64> for Exponential
impl Cdf<f32> for Exponential
impl Cdf<f32> for Gamma
impl Cdf<f64> for Gamma
impl Cdf<f32> for Gaussian
impl Cdf<f64> for Gaussian
impl Cdf<f32> for InvGamma
impl Cdf<f64> for InvGamma
impl Cdf<f64> for Laplace
impl Cdf<f32> for Laplace
impl Cdf<u16> for Poisson
impl Cdf<u32> for Poisson
impl Cdf<f64> for Uniform
impl Cdf<f32> for Uniform