Crate rv[−][src]

Provides random variables for probabilistic modeling.

The dist module provides a number of probability distributions with various traits implemented in the traits module. You can do all the standard probability distribution stuff like evaluate the PDF/PMF and draw values of different types.

The prelude module provides all the distributions, all the traits, and creates a few useful type aliases.

Design

Random variables are designed to be flexible. For example, we don't just want a Beta distribution that works with f64; we want it to work with a bunch of things

extern crate rand;
extern crate rv;

use rv::prelude::*;

// Beta(0.5, 0.5)
let beta = Beta::jeffreys();

let mut rng = rand::thread_rng();

// 100 f64 weights in (0, 1)
let f64s: Vec<f64> = beta.sample(100, &mut rng);
let pdf_x = beta.ln_pdf(&f64s[42]);

// 100 f32 weights in (0, 1)
let f32s: Vec<f32> = beta.sample(100, &mut rng);
let pdf_y = beta.ln_pdf(&f32s[42]);

// 100 Bernoulli distributions -- Beta is a prior on the weight
let berns: Vec<Bernoulli> = beta.sample(100, &mut rng);
let pdf_bern = beta.ln_pdf(&berns[42]);

Examples

For more examples, check out the examples directory.

Conjugate analysis of coin flips

extern crate rand;
extern crate rv;

use rand::Rng;
use rv::prelude::*;

fn main() {
    let mut rng = rand::thread_rng();

    // A sequence of observations
    let flips = vec![true, false, true, true, true, false, true];

    // Construct the Jeffreys prior of Beta(0.5, 0.5)
    let prior = Beta::jeffreys();

    // Packages the data in a wrapper that marks it as having come from
    // Bernoulli trials.
    let obs: BernoulliData<bool> = DataOrSuffStat::Data(&flips);

    // Generate the posterior distribution P(θ|x); the distribution of
    // probable coin weights
    let posterior: Beta = prior.posterior(&obs);

    // What is the probability that the next flip would come up heads
    // (true) given the observed flips (posterior predictive)?
    let p_heads = prior.pp(&true, &obs);
}

Modules

consts	Mathematical constants
data	Data utilities
dist	Probability distributions
misc	Random utilities
prelude	Re-imports for convenience
traits	Trait definitions

Structs

ConjugateModel