rv 0.20.1

Random variables
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use std::hint::black_box;

use criterion::BatchSize;
use criterion::Criterion;
use criterion::{criterion_group, criterion_main};
use rv::data::GaussianSuffStat;
use rv::dist::Gaussian;
use rv::dist::NormalGamma;
use rv::traits::{ConjugatePrior, DataOrSuffStat, Sampleable, SuffStat};

fn bench_ng_postpred(c: &mut Criterion) {
    let mut group = c.benchmark_group("NG ln pp(x)");
    let ng = NormalGamma::new_unchecked(0.1, 1.2, 2.3, 3.4);
    let mut rng = rand::rng();
    let g = Gaussian::standard();

    group.bench_function("No cache".to_string(), |b| {
        b.iter_batched(
            || {
                let stat = {
                    let mut stat = GaussianSuffStat::new();
                    g.sample_stream(&mut rng).take(10).for_each(|x: f64| {
                        stat.observe(&x);
                    });
                    stat
                };
                let y: f64 = g.draw(&mut rng);
                (y, stat)
            },
            |(y, stat)| {
                black_box(ng.ln_pp(&y, DataOrSuffStat::SuffStat(&stat)))
            },
            BatchSize::SmallInput,
        );
    });

    group.bench_function("With cache".to_string(), |b| {
        b.iter_batched(
            || {
                let stat = {
                    let mut stat = GaussianSuffStat::new();
                    g.sample_stream(&mut rng).take(10).for_each(|x: f64| {
                        stat.observe(&x);
                    });
                    stat
                };
                let y: f64 = g.draw(&mut rng);

                let stat: DataOrSuffStat<f64, _> =
                    DataOrSuffStat::SuffStat(&stat);
                let cache = ng.ln_pp_cache(stat);
                (y, cache)
            },
            |(y, cache)| black_box(ng.ln_pp_with_cache(&cache, &y)),
            BatchSize::SmallInput,
        );
    });
}

criterion_group!(ng_benches, bench_ng_postpred);
criterion_main!(ng_benches);