[−][src]Trait rv::traits::Cdf
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
Loading content...Implementors
impl Cdf<bool> for Bernoulli
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impl Cdf<f32> for Beta
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impl Cdf<f32> for Cauchy
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impl Cdf<f32> for ChiSquared
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impl Cdf<f32> for Exponential
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impl Cdf<f32> for Gamma
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impl Cdf<f32> for Gaussian
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impl Cdf<f32> for Gev
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impl Cdf<f32> for InvGamma
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impl Cdf<f32> for KsTwoAsymptotic
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impl Cdf<f32> for Kumaraswamy
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impl Cdf<f32> for Laplace
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impl Cdf<f32> for LogNormal
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impl Cdf<f32> for Pareto
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impl Cdf<f32> for Uniform
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impl Cdf<f32> for VonMises
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impl Cdf<f64> for Beta
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impl Cdf<f64> for Cauchy
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impl Cdf<f64> for ChiSquared
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impl Cdf<f64> for Exponential
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impl Cdf<f64> for Gamma
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impl Cdf<f64> for Gaussian
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impl Cdf<f64> for Gev
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impl Cdf<f64> for InvGamma
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impl Cdf<f64> for KsTwoAsymptotic
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impl Cdf<f64> for Kumaraswamy
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impl Cdf<f64> for Laplace
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impl Cdf<f64> for LogNormal
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impl Cdf<f64> for Pareto
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impl Cdf<f64> for Uniform
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impl Cdf<f64> for VonMises
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impl Cdf<i16> for BetaBinomial
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impl Cdf<i16> for Binomial
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impl Cdf<i32> for BetaBinomial
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impl Cdf<i32> for Binomial
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impl Cdf<i64> for BetaBinomial
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impl Cdf<i64> for Binomial
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impl Cdf<i8> for BetaBinomial
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impl Cdf<i8> for Binomial
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impl Cdf<u16> for BetaBinomial
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impl Cdf<u16> for Binomial
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impl Cdf<u16> for Poisson
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impl Cdf<u32> for BetaBinomial
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impl Cdf<u32> for Binomial
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impl Cdf<u32> for Poisson
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impl Cdf<u64> for BetaBinomial
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impl Cdf<u64> for Binomial
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impl Cdf<u8> for BetaBinomial
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impl Cdf<u8> for Binomial
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impl Cdf<u8> for Poisson
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impl Cdf<usize> for BetaBinomial
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impl Cdf<usize> for Binomial
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impl<Fx, X> Cdf<X> for Fx where
Fx: Deref,
Fx::Target: Cdf<X>,
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Fx: Deref,
Fx::Target: Cdf<X>,
impl<X> Cdf<X> for Geometric where
X: Unsigned + Integer + FromPrimitive + ToPrimitive + Saturating + Bounded,
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X: Unsigned + Integer + FromPrimitive + ToPrimitive + Saturating + Bounded,
impl<X, Fx> Cdf<X> for Mixture<Fx> where
Fx: Rv<X> + Cdf<X>,
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Fx: Rv<X> + Cdf<X>,
impl<X, T> Cdf<X> for DiscreteUniform<T> where
X: Integer + From<T> + ToPrimitive + Copy,
T: DuParam + SampleUniform + ToPrimitive,
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X: Integer + From<T> + ToPrimitive + Copy,
T: DuParam + SampleUniform + ToPrimitive,