Trait rv::traits::Mean

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

Defines the distribution mean

Required Methods

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

Returns None if the mean is undefined

Implementors

impl Mean<f32> for Beta

impl Mean<f32> for ChiSquared

impl Mean<f32> for Exponential

impl Mean<f32> for Gamma

impl Mean<f32> for Gaussian

impl Mean<f32> for Gev

impl Mean<f32> for InvChiSquared

impl Mean<f32> for InvGamma

impl Mean<f32> for InvGaussian

impl Mean<f32> for Kumaraswamy

impl Mean<f32> for Laplace

impl Mean<f32> for LogNormal

impl Mean<f32> for Pareto

impl Mean<f32> for ScaledInvChiSquared

impl Mean<f32> for StudentsT

impl Mean<f32> for Uniform

impl Mean<f32> for UnitPowerLaw

impl Mean<f32> for VonMises

impl Mean<f64> for Bernoulli

impl Mean<f64> for Beta

impl Mean<f64> for BetaBinomial

impl Mean<f64> for Binomial

impl Mean<f64> for ChiSquared

impl Mean<f64> for Empirical

impl Mean<f64> for Exponential

impl Mean<f64> for Gamma

impl Mean<f64> for Gaussian

impl Mean<f64> for Geometric

impl Mean<f64> for Gev

impl Mean<f64> for InvChiSquared

impl Mean<f64> for InvGamma

impl Mean<f64> for InvGaussian

impl Mean<f64> for Kumaraswamy

impl Mean<f64> for Laplace

impl Mean<f64> for LogNormal

impl Mean<f64> for NegBinomial

impl Mean<f64> for Pareto

impl Mean<f64> for Poisson

impl Mean<f64> for ScaledInvChiSquared

impl Mean<f64> for Skellam

impl Mean<f64> for StudentsT

impl Mean<f64> for Uniform

impl Mean<f64> for UnitPowerLaw

impl Mean<f64> for VonMises

impl Mean<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>> for MvGaussian

impl Mean<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>> for InvWishart

impl<Fx> Mean<f32> for Mixture<Fx>

where Fx: Mean<f32>,

impl<Fx> Mean<f64> for Mixture<Fx>

where Fx: Mean<f64>,

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

where K: Kernel,

impl<T> Mean<f64> for DiscreteUniform<T>

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