Crate rv

Source
Expand description

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.

§Features

  • serde1: enables serialization and de-serialization of structs via serde
  • process: Gives you access to Gaussian processes.
  • arraydist: Enables distributions and statistical tests that require the nalgebra crate.
  • experimental: Enables experimental features.

§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

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

use rv::prelude::*;

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);

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