r-gen

A probabilistic programming framework for the rust programming language. This is heavily based on the Gen library for the Julia programming language.

Example using importance sampling to estimate latent variables. ```rust ... fn main() { //Define our generative model. #[rgen] fn mymodel(():()){ let p = sample!(format!("p"), Distribution::Beta(1.0, 1.0)); sample!(format!("num_heads"), Distribution::Binomial(100, p.into())); }

//Run the model once in the forward direction and record the observations. 
let (t, _) : (Trace, _)= simulate(&mut my_model, ());
let choices = Choicemap::from(vec![("num_heads", t.choices["num_heads"])]);

//Perform importance resampling to get an estimate for the value of p. 
let mut traces = Vec::new();
for _ in 0..1000 {
    let (gt, _) : (Trace, _)= generate(&mut my_model, (), &choices);
    traces.push(gt); 
}

println!("Actual value for p:\t {}", t.choices["p"]); 
println!("Generated value for p:\t {}", Trace::sample_weighted_traces(&traces).unwrap().choices["p"]);

} Outputs: Actual value for p: 0.8011431168181488 Generated value for p: 0.7879998086169554 ```

Note the #[r_gen] tag on the generative function. This must be on any of your generative functions in order to use simulate and generate on them. One other stipulation of a generative function is that it must have a single argument. This can be a tuple or () if you dont need to pass any arguments. In order to make a random choice (sample from a distribution) you must use the sample!() macro. The syntax for this is rust sample!(identifier, Distribution); This samples the Distribution given and stores the result in identifier. Distribution is defined as rust pub enum Distribution { Bernoulli(f64), //p Binomial(i64, f64), //n, p Normal(f64, f64), //mu, sigma Gamma(f64, f64), //alpha, beta Beta(f64, f64), //alpha, beta LogNormal(f64, f64), //mu, sigma Categorical(Vec<f64>), //p Dirichlet(Vec<f64>), //alpha } Value is defined as rust pub enum Value { Boolean(bool), Integer(i64), Real(f64), Vector(Vec<Value>) } The variant of the enum that is returned from sampling depends on the distribution being sampled.

This framework is in EARLY stages of development. Everything is subject to change.