Trait rv::traits::Variance

pub trait Variance<X> {
    // Required method
    fn variance(&self) -> Option<X>;
}

Defines the distribution variance

Required Methods

fn variance(&self) -> Option<X>

Returns None if the variance is undefined

Implementors

impl Variance<f32> for ChiSquared

impl Variance<f32> for Exponential

impl Variance<f32> for InvChiSquared

impl Variance<f32> for Laplace

impl Variance<f32> for ScaledInvChiSquared

impl Variance<f32> for StudentsT

impl Variance<f32> for Uniform

impl Variance<f32> for VonMises

impl Variance<f64> for Bernoulli

impl Variance<f64> for Beta

impl Variance<f64> for BetaBinomial

impl Variance<f64> for Binomial

impl Variance<f64> for ChiSquared

impl Variance<f64> for Empirical

impl Variance<f64> for Exponential

impl Variance<f64> for Gamma

impl Variance<f64> for Gaussian

impl Variance<f64> for Geometric

impl Variance<f64> for Gev

impl Variance<f64> for InvChiSquared

impl Variance<f64> for InvGamma

impl Variance<f64> for InvGaussian

impl Variance<f64> for Laplace

impl Variance<f64> for LogNormal

impl Variance<f64> for NegBinomial

impl Variance<f64> for Pareto

impl Variance<f64> for Poisson

impl Variance<f64> for ScaledInvChiSquared

impl Variance<f64> for Skellam

impl Variance<f64> for StudentsT

impl Variance<f64> for Uniform

impl Variance<f64> for UnitPowerLaw

impl Variance<f64> for VonMises

impl Variance<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>> for MvGaussian

impl<Fx> Variance<f32> for Mixture<Fx>

where Fx: Mean<f32> + Variance<f32>,

impl<Fx> Variance<f64> for Mixture<Fx>

where Fx: Mean<f64> + Variance<f64>,

impl<K> Variance<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>> for GaussianProcessPrediction<K>

where K: Kernel,

impl<T> Variance<f64> for DiscreteUniform<T>

where T: DuParam + SampleUniform + Into<f64>,