Trait statrs::statistics::Distribution

source · [−]
pub trait Distribution<T: Float>: Distribution<T> {
    fn mean(&self) -> Option<T> { ... }
    fn variance(&self) -> Option<T> { ... }
    fn std_dev(&self) -> Option<T> { ... }
    fn entropy(&self) -> Option<T> { ... }
    fn skewness(&self) -> Option<T> { ... }
}

Provided Methods

Returns the mean, if it exists. The default implementation returns an estimation based on random samples. This is a crude estimate for when no further information is known about the distribution. More accurate statements about the mean can and should be given by overriding the default implementation.

Examples
use statrs::statistics::Distribution;
use statrs::distribution::Uniform;

let n = Uniform::new(0.0, 1.0).unwrap();
assert_eq!(0.5, n.mean().unwrap());

Returns the variance, if it exists. The default implementation returns an estimation based on random samples. This is a crude estimate for when no further information is known about the distribution. More accurate statements about the variance can and should be given by overriding the default implementation.

Examples
use statrs::statistics::Distribution;
use statrs::distribution::Uniform;

let n = Uniform::new(0.0, 1.0).unwrap();
assert_eq!(1.0 / 12.0, n.variance().unwrap());

Returns the standard deviation, if it exists.

Examples
use statrs::statistics::Distribution;
use statrs::distribution::Uniform;

let n = Uniform::new(0.0, 1.0).unwrap();
assert_eq!((1f64 / 12f64).sqrt(), n.std_dev().unwrap());

Returns the entropy, if it exists.

Examples
use statrs::statistics::Distribution;
use statrs::distribution::Uniform;

let n = Uniform::new(0.0, 1.0).unwrap();
assert_eq!(0.0, n.entropy().unwrap());

Returns the skewness, if it exists.

Examples
use statrs::statistics::Distribution;
use statrs::distribution::Uniform;

let n = Uniform::new(0.0, 1.0).unwrap();
assert_eq!(0.0, n.skewness().unwrap());

Implementors