[−][src]Trait rv::traits::ConjugatePrior

pub trait ConjugatePrior<X, Fx>: Rv<Fx> where
    Fx: Rv<X> + HasSuffStat<X>, {
    type Posterior: Rv<Fx>;
    fn posterior(&self, x: &DataOrSuffStat<X, Fx>) -> Self::Posterior;
    fn ln_m(&self, x: &DataOrSuffStat<X, Fx>) -> f64;
    fn ln_pp(&self, y: &X, x: &DataOrSuffStat<X, Fx>) -> f64;

    fn m(&self, x: &DataOrSuffStat<X, Fx>) -> f64 { ... }
    fn pp(&self, y: &X, x: &DataOrSuffStat<X, Fx>) -> f64 { ... }
}

A prior on Fx that induces a posterior that is the same form as the prior

Example

Conjugate analysis of coin flips using Bernoulli with a Beta prior on the success probability.

use rv::traits::ConjugatePrior;
use rv::dist::{Bernoulli, Beta};

let flips = vec![true, false, false];
let prior = Beta::jeffreys();

// If we observe more false than true, the posterior predictive
// probability of true decreases.
let pp_no_obs = prior.pp(&true, &(&vec![]).into());
let pp_obs = prior.pp(&true, &(&flips).into());

assert!(pp_obs < pp_no_obs);

Associated Types

`type Posterior: Rv<Fx>`

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Required methods

`fn posterior(&self, x: &DataOrSuffStat<X, Fx>) -> Self::Posterior`

Computes the posterior distribution from the data

`fn ln_m(&self, x: &DataOrSuffStat<X, Fx>) -> f64`

Log marginal likelihood

`fn ln_pp(&self, y: &X, x: &DataOrSuffStat<X, Fx>) -> f64`

Log posterior predictive of y given x

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Provided methods

`fn m(&self, x: &DataOrSuffStat<X, Fx>) -> f64`

Marginal likelihood of x

`fn pp(&self, y: &X, x: &DataOrSuffStat<X, Fx>) -> f64`

Posterior Predictive distribution

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Implementors

`impl ConjugatePrior<bool, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<bool, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<bool, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &bool, x: &DataOrSuffStat<bool, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<f64, Gaussian> for NormalGamma`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<f64, Gaussian>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<f64, Gaussian>) -> f64`[src]

`fn ln_pp(&self, y: &f64, x: &DataOrSuffStat<f64, Gaussian>) -> f64`[src]

`impl ConjugatePrior<i16, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<i16, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<i16, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &i16, x: &DataOrSuffStat<i16, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<i32, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<i32, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<i32, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &i32, x: &DataOrSuffStat<i32, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<i64, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<i64, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<i64, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &i64, x: &DataOrSuffStat<i64, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<i8, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<i8, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<i8, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &i8, x: &DataOrSuffStat<i8, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<isize, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<isize, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<isize, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &isize, x: &DataOrSuffStat<isize, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<u16, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<u16, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<u16, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &u16, x: &DataOrSuffStat<u16, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<u16, Poisson> for Gamma`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<u16, Poisson>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<u16, Poisson>) -> f64`[src]

`fn ln_pp(&self, y: &u16, x: &DataOrSuffStat<u16, Poisson>) -> f64`[src]

`impl ConjugatePrior<u32, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<u32, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<u32, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &u32, x: &DataOrSuffStat<u32, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<u32, Poisson> for Gamma`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<u32, Poisson>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<u32, Poisson>) -> f64`[src]

`fn ln_pp(&self, y: &u32, x: &DataOrSuffStat<u32, Poisson>) -> f64`[src]

`impl ConjugatePrior<u64, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<u64, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<u64, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &u64, x: &DataOrSuffStat<u64, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<u8, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<u8, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<u8, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &u8, x: &DataOrSuffStat<u8, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<u8, Poisson> for Gamma`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<u8, Poisson>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<u8, Poisson>) -> f64`[src]

`fn ln_pp(&self, y: &u8, x: &DataOrSuffStat<u8, Poisson>) -> f64`[src]

`impl ConjugatePrior<usize, Bernoulli> for Beta`[src]

`type Posterior = Self`

`fn posterior(&self, x: &DataOrSuffStat<usize, Bernoulli>) -> Self`[src]

`fn ln_m(&self, x: &DataOrSuffStat<usize, Bernoulli>) -> f64`[src]

`fn ln_pp(&self, y: &usize, x: &DataOrSuffStat<usize, Bernoulli>) -> f64`[src]

`impl ConjugatePrior<Matrix<f64, Dynamic, U1, VecStorage<f64, Dynamic, U1>>, MvGaussian> for NormalInvWishart`[src]

`type Posterior = NormalInvWishart`

`fn posterior( &self, x: &DataOrSuffStat<'a, DVector<f64>, MvGaussian> ) -> NormalInvWishart`[src]

`fn ln_m(&self, x: &DataOrSuffStat<'a, DVector<f64>, MvGaussian>) -> f64`[src]

`fn ln_pp( &self, y: &DVector<f64>, x: &DataOrSuffStat<'a, DVector<f64>, MvGaussian> ) -> f64`[src]

`impl<X: CategoricalDatum> ConjugatePrior<X, Categorical> for Dirichlet`[src]

`type Posterior = Self`

`fn posterior(&self, x: &CategoricalData<X>) -> Self::Posterior`[src]

`fn ln_m(&self, x: &CategoricalData<X>) -> f64`[src]

`fn ln_pp(&self, y: &X, x: &CategoricalData<X>) -> f64`[src]

`impl<X: CategoricalDatum> ConjugatePrior<X, Categorical> for SymmetricDirichlet`[src]

`type Posterior = Dirichlet`

`fn posterior(&self, x: &CategoricalData<X>) -> Self::Posterior`[src]

`fn ln_m(&self, x: &CategoricalData<X>) -> f64`[src]

`fn ln_pp(&self, y: &X, x: &CategoricalData<X>) -> f64`[src]

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