[−][src]Trait rv::traits::Rv
Random variable
Contains the minimal functionality that a random object must have to be useful: a function defining the un-normalized density/mass at a point, and functions to draw samples from the distribution.
Required methods
fn ln_f(&self, x: &X) -> f64
Probability function
Example
use rv::dist::Gaussian; use rv::traits::Rv; let g = Gaussian::standard(); assert!(g.ln_f(&0.0_f64) > g.ln_f(&0.1_f64)); assert!(g.ln_f(&0.0_f64) > g.ln_f(&-0.1_f64));
fn draw<R: Rng>(&self, rng: &mut R) -> X
Single draw from the Rv
Example
Flip a coin
extern crate rand; use rv::dist::Bernoulli; use rv::traits::Rv; let b = Bernoulli::uniform(); let mut rng = rand::thread_rng(); let x: bool = b.draw(&mut rng); // could be true, could be false.
Provided methods
fn f(&self, x: &X) -> f64
Probability function
Example
use rv::dist::Gaussian; use rv::traits::Rv; let g = Gaussian::standard(); assert!(g.f(&0.0_f64) > g.f(&0.1_f64)); assert!(g.f(&0.0_f64) > g.f(&-0.1_f64));
fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
Multiple draws of the Rv
Example
Flip a lot of coins
extern crate rand; use rv::dist::Bernoulli; use rv::traits::Rv; let b = Bernoulli::uniform(); let mut rng = rand::thread_rng(); let xs: Vec<bool> = b.sample(22, &mut rng); assert_eq!(xs.len(), 22);
Implementors
impl Rv<bool> for Bernoulli
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fn f(&self, x: &bool) -> f64
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fn ln_f(&self, x: &bool) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> bool
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<bool>
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impl Rv<f32> for Beta
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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impl Rv<f32> for Cauchy
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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impl Rv<f32> for ChiSquared
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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impl Rv<f32> for Exponential
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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impl Rv<f32> for Gamma
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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impl Rv<f32> for Gaussian
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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impl Rv<f32> for Gev
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<f32> for InvGamma
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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impl Rv<f32> for Laplace
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<f32> for LogNormal
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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impl Rv<f32> for Pareto
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<f32> for StudentsT
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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impl Rv<f32> for Uniform
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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impl Rv<f32> for VonMises
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<f64> for Beta
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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impl Rv<f64> for Cauchy
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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impl Rv<f64> for ChiSquared
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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impl Rv<f64> for Exponential
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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impl Rv<f64> for Gamma
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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impl Rv<f64> for Gaussian
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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impl Rv<f64> for Gev
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<f64> for InvGamma
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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impl Rv<f64> for Laplace
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<f64> for LogNormal
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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impl Rv<f64> for Pareto
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<f64> for StudentsT
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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impl Rv<f64> for Uniform
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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impl Rv<f64> for VonMises
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<i16> for Bernoulli
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fn f(&self, x: &i16) -> f64
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fn ln_f(&self, x: &i16) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i16
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i16>
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impl Rv<i16> for BetaBinomial
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fn ln_f(&self, k: &i16) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i16
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i16>
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fn f(&self, x: &X) -> f64
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impl Rv<i16> for Binomial
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fn ln_f(&self, k: &i16) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i16
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<i32> for Bernoulli
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fn f(&self, x: &i32) -> f64
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fn ln_f(&self, x: &i32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i32>
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impl Rv<i32> for BetaBinomial
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fn ln_f(&self, k: &i32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i32>
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fn f(&self, x: &X) -> f64
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impl Rv<i32> for Binomial
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fn ln_f(&self, k: &i32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i32
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<i64> for Bernoulli
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fn f(&self, x: &i64) -> f64
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fn ln_f(&self, x: &i64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i64>
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impl Rv<i64> for BetaBinomial
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fn ln_f(&self, k: &i64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i64>
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fn f(&self, x: &X) -> f64
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impl Rv<i64> for Binomial
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fn ln_f(&self, k: &i64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i64
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<i8> for Bernoulli
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fn f(&self, x: &i8) -> f64
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fn ln_f(&self, x: &i8) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i8
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i8>
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impl Rv<i8> for BetaBinomial
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fn ln_f(&self, k: &i8) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i8
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i8>
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fn f(&self, x: &X) -> f64
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impl Rv<i8> for Binomial
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fn ln_f(&self, k: &i8) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> i8
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<isize> for Bernoulli
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fn f(&self, x: &isize) -> f64
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fn ln_f(&self, x: &isize) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> isize
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<isize>
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impl Rv<u16> for Bernoulli
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fn f(&self, x: &u16) -> f64
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fn ln_f(&self, x: &u16) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u16
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u16>
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impl Rv<u16> for BetaBinomial
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fn ln_f(&self, k: &u16) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u16
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u16>
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fn f(&self, x: &X) -> f64
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impl Rv<u16> for Binomial
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fn ln_f(&self, k: &u16) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u16
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<u16> for Poisson
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fn ln_f(&self, x: &u16) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u16
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u16>
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fn f(&self, x: &X) -> f64
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impl Rv<u32> for Bernoulli
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fn f(&self, x: &u32) -> f64
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fn ln_f(&self, x: &u32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u32>
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impl Rv<u32> for BetaBinomial
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fn ln_f(&self, k: &u32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u32>
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fn f(&self, x: &X) -> f64
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impl Rv<u32> for Binomial
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fn ln_f(&self, k: &u32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u32
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<u32> for Poisson
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fn ln_f(&self, x: &u32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u32>
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fn f(&self, x: &X) -> f64
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impl Rv<u64> for Bernoulli
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fn f(&self, x: &u64) -> f64
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fn ln_f(&self, x: &u64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u64>
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impl Rv<u64> for BetaBinomial
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fn ln_f(&self, k: &u64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u64>
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fn f(&self, x: &X) -> f64
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impl Rv<u64> for Binomial
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fn ln_f(&self, k: &u64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u64
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<u8> for Bernoulli
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fn f(&self, x: &u8) -> f64
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fn ln_f(&self, x: &u8) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u8
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u8>
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impl Rv<u8> for BetaBinomial
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fn ln_f(&self, k: &u8) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u8
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u8>
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fn f(&self, x: &X) -> f64
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impl Rv<u8> for Binomial
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fn ln_f(&self, k: &u8) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u8
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<u8> for Poisson
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fn ln_f(&self, x: &u8) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> u8
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u8>
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fn f(&self, x: &X) -> f64
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impl Rv<usize> for Bernoulli
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fn f(&self, x: &usize) -> f64
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fn ln_f(&self, x: &usize) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> usize
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<usize>
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impl Rv<usize> for BetaBinomial
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fn ln_f(&self, k: &usize) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> usize
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<usize>
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fn f(&self, x: &X) -> f64
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impl Rv<usize> for Binomial
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fn ln_f(&self, k: &usize) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> usize
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<Partition> for Crp
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fn ln_f(&self, x: &Partition) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> Partition
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<Bernoulli> for Beta
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fn ln_f(&self, x: &Bernoulli) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> Bernoulli
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<Categorical> for Dirichlet
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fn ln_f(&self, x: &Categorical) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> Categorical
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<Categorical> for SymmetricDirichlet
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fn ln_f(&self, x: &Categorical) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> Categorical
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<Gaussian> for NormalGamma
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fn ln_f(&self, x: &Gaussian) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> Gaussian
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<MvGaussian> for NormalInvWishart
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fn ln_f(&self, x: &MvGaussian) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> MvGaussian
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<Vec<f64>> for SymmetricDirichlet
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fn draw<R: Rng>(&self, rng: &mut R) -> Vec<f64>
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fn ln_f(&self, x: &Vec<f64>) -> f64
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<Vec<f64>> for Dirichlet
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fn draw<R: Rng>(&self, rng: &mut R) -> Vec<f64>
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fn ln_f(&self, x: &Vec<f64>) -> f64
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl Rv<Matrix<f64, Dynamic, Dynamic, <DefaultAllocator as Allocator<f64, Dynamic, Dynamic>>::Buffer>> for InvWishart
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fn ln_f(&self, x: &DMatrix<f64>) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> DMatrix<f64>
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<DMatrix<f64>>
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fn f(&self, x: &X) -> f64
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impl Rv<Matrix<f64, Dynamic, U1, VecStorage<f64, Dynamic, U1>>> for MvGaussian
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fn ln_f(&self, x: &DVector<f64>) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> DVector<f64>
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl<X> Rv<X> for Geometric where
X: Unsigned + Integer + FromPrimitive + ToPrimitive + Saturating + Bounded,
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X: Unsigned + Integer + FromPrimitive + ToPrimitive + Saturating + Bounded,
fn ln_f(&self, k: &X) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> X
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl<X, Fx> Rv<X> for Mixture<Fx> where
Fx: Rv<X> + ApiReady,
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Fx: Rv<X> + ApiReady,
fn ln_f(&self, x: &X) -> f64
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fn f(&self, x: &X) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> X
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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impl<X, Fx, Pr> Rv<X> for ConjugateModel<X, Fx, Pr> where
X: ApiReady,
Fx: Rv<X> + HasSuffStat<X> + ApiReady,
Pr: ConjugatePrior<X, Fx> + ApiReady,
Fx::Stat: ApiReady,
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X: ApiReady,
Fx: Rv<X> + HasSuffStat<X> + ApiReady,
Pr: ConjugatePrior<X, Fx> + ApiReady,
Fx::Stat: ApiReady,
fn ln_f(&self, x: &X) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> X
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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fn f(&self, x: &X) -> f64
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impl<X, T> Rv<X> for DiscreteUniform<T> where
T: Integer + SampleUniform + Copy,
X: Integer + From<T>,
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T: Integer + SampleUniform + Copy,
X: Integer + From<T>,