Struct rv::ConjugateModel

pub struct ConjugateModel<X, Fx, Pr>
where
    Fx: Rv<X> + HasSuffStat<X>,
    Pr: ConjugatePrior<X, Fx>,
{ /* private fields */ }

A wrapper for a complete conjugate model

Paramters

X: The type of the data/observations to be modeled Fx: The type of the likelihood, f(x|θ) Pr: The type of the prior on the parameters of Fx, π(θ)

Implementations

impl<X, Fx, Pr> ConjugateModel<X, Fx, Pr>

where Fx: Rv<X> + HasSuffStat<X>, Pr: ConjugatePrior<X, Fx>,

pub fn new(fx: &Fx, pr: Arc<Pr>) -> Self

Create a new conjugate model

Arguments

fx:

Example

use std::sync::Arc;
use rv::prelude::*;
use rv::ConjugateModel;

let pr = Arc::new(Beta::jeffreys());
let fx = Bernoulli::uniform();
let model = ConjugateModel::<bool, Bernoulli, Beta>::new(&fx, pr);

pub fn ln_m(&self) -> f64

Log marginal likelihood, f(obs)

pub fn ln_pp(&self, y: &X) -> f64

Log posterior predictive, f(y|obs)

pub fn posterior(&self) -> Pr::Posterior

Return the posterior distribution

Example

use std::sync::Arc;
use rv::prelude::*;
use rv::ConjugateModel;

let flips: Vec<bool> = vec![true, false, true, false, false, false];

let pr = Arc::new(Beta::new(1.0, 1.0).unwrap());
let fx = Bernoulli::uniform();
let mut model = ConjugateModel::<bool, Bernoulli, Beta>::new(&fx, pr);

model.observe_many(&flips);

let post = model.posterior();

assert_eq!(post, Beta::new(3.0, 5.0).unwrap());

Trait Implementations

impl<X: Clone, Fx, Pr> Clone for ConjugateModel<X, Fx, Pr>

where Fx: Rv<X> + HasSuffStat<X> + Clone, Pr: ConjugatePrior<X, Fx> + Clone, Fx::Stat: Clone,

fn clone(&self) -> ConjugateModel<X, Fx, Pr>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<X: Debug, Fx, Pr> Debug for ConjugateModel<X, Fx, Pr>

where Fx: Rv<X> + HasSuffStat<X> + Debug, Pr: ConjugatePrior<X, Fx> + Debug, Fx::Stat: Debug,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<X: PartialEq, Fx, Pr> PartialEq for ConjugateModel<X, Fx, Pr>

where Fx: Rv<X> + HasSuffStat<X> + PartialEq, Pr: ConjugatePrior<X, Fx> + PartialEq, Fx::Stat: PartialEq,

fn eq(&self, other: &ConjugateModel<X, Fx, Pr>) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<X: PartialOrd, Fx, Pr> PartialOrd for ConjugateModel<X, Fx, Pr>

where Fx: Rv<X> + HasSuffStat<X> + PartialOrd, Pr: ConjugatePrior<X, Fx> + PartialOrd, Fx::Stat: PartialOrd,

fn partial_cmp(&self, other: &ConjugateModel<X, Fx, Pr>) -> Option<Ordering>

This method returns an ordering between self and other values if one exists.

fn lt(&self, other: &Rhs) -> bool

This method tests less than (for self and other) and is used by the < operator.

fn le(&self, other: &Rhs) -> bool

This method tests less than or equal to (for self and other) and is used by the <= operator.

fn gt(&self, other: &Rhs) -> bool

This method tests greater than (for self and other) and is used by the > operator.

fn ge(&self, other: &Rhs) -> bool

This method tests greater than or equal to (for self and other) and is used by the >= operator.

impl<X, Fx, Pr> Rv<X> for ConjugateModel<X, Fx, Pr>

where Fx: Rv<X> + HasSuffStat<X>, Pr: ConjugatePrior<X, Fx>,

fn ln_f(&self, x: &X) -> f64

Probability function

fn draw<R: Rng>(&self, rng: &mut R) -> X

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>

Multiple draws of the Rv

fn f(&self, x: &X) -> f64

Probability function

fn sample_stream<'r, R: Rng>( &'r self, rng: &'r mut R ) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl<X, Fx, Pr> SuffStat<X> for ConjugateModel<X, Fx, Pr>

where Fx: Rv<X> + HasSuffStat<X>, Pr: ConjugatePrior<X, Fx>,

fn n(&self) -> usize

Returns the number of observations

fn observe(&mut self, x: &X)

Assimilate the datum x into the statistic

fn forget(&mut self, x: &X)

Remove the datum x from the statistic

fn observe_many(&mut self, xs: &[X])

Assimilate several observations

fn forget_many(&mut self, xs: &[X])

Forget several observations

impl<X, Fx, Pr> StructuralPartialEq for ConjugateModel<X, Fx, Pr>

where Fx: Rv<X> + HasSuffStat<X>, Pr: ConjugatePrior<X, Fx>,