Trait rv::traits::ConjugatePrior

source ·

pub trait ConjugatePrior<X, Fx>: Sampleable<Fx>where
    Fx: HasDensity<X> + HasSuffStat<X>,{
    type Posterior: Sampleable<Fx>;
    type MCache;
    type PpCache;

    // Required methods
    fn posterior(&self, x: &DataOrSuffStat<'_, X, Fx>) -> Self::Posterior;
    fn ln_m_cache(&self) -> Self::MCache;
    fn ln_m_with_cache(
        &self,
        cache: &Self::MCache,
        x: &DataOrSuffStat<'_, X, Fx>,
    ) -> f64;
    fn ln_pp_cache(&self, x: &DataOrSuffStat<'_, X, Fx>) -> Self::PpCache;
    fn ln_pp_with_cache(&self, cache: &Self::PpCache, y: &X) -> f64;

    // Provided methods
    fn posterior_from_suffstat(&self, stat: &Fx::Stat) -> 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_with_cache(&self, cache: &Self::PpCache, y: &X) -> f64 { ... }
    fn pp(&self, y: &X, x: &DataOrSuffStat<'_, X, Fx>) -> f64 { ... }
}

Expand description

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);

Use a cache to speed up repeated computations.

use rv::traits::*;
use rv::traits::SuffStat;
use rv::dist::{Categorical, SymmetricDirichlet};
use rv::data::{CategoricalSuffStat, DataOrSuffStat};
use std::time::Instant;

let ncats = 10;
let symdir = SymmetricDirichlet::jeffreys(ncats).unwrap();
let mut suffstat = CategoricalSuffStat::new(10);
let mut rng = rand::thread_rng();

Categorical::new(&vec![1.0, 1.0, 5.0, 1.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0])
    .unwrap()
    .sample_stream(&mut rng)
    .take(1000)
    .for_each(|x: u8| suffstat.observe(&x));


let stat = DataOrSuffStat::SuffStat(&suffstat);

// Get predictions from predictive distribution using the cache
let t_cache = {
    let t_start = Instant::now();
    let cache = symdir.ln_pp_cache(&stat);
    // Argmax
    let k_max = (0..ncats).fold((0, f64::NEG_INFINITY), |(ix, f), y| {
            let f_r = symdir.ln_pp_with_cache(&cache, &y);
            if f_r > f {
                (y, f_r)
            } else {
                (ix, f)
            }

        });

    assert_eq!(k_max.0, 2);
    t_start.elapsed()
};

// Get predictions from predictive distribution w/o cache
let t_no_cache = {
    let t_start = Instant::now();
    // Argmax
    let k_max = (0..ncats).fold((0, f64::NEG_INFINITY), |(ix, f), y| {
            let f_r = symdir.ln_pp(&y, &stat);
            if f_r > f {
                (y, f_r)
            } else {
                (ix, f)
            }

        });

    assert_eq!(k_max.0, 2);
    t_start.elapsed()
};

// Using cache improves runtime
assert!(t_no_cache.as_nanos() > t_cache.as_nanos());