# 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

```use rv::prelude::*;

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

// 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::*;

// 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)?