https://github.com/obsidiandynamics/metromc

Markov chain Monte Carlo sampling
https://github.com/obsidiandynamics/metromc

mcmc random sampling

Last synced: about 2 months ago
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Markov chain Monte Carlo sampling

• Host: GitHub
• URL: https://github.com/obsidiandynamics/metromc
• Owner: obsidiandynamics
• License: mit
• Created: 2024-01-03T18:58:47.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2024-01-04T06:21:05.000Z (about 1 year ago)
• Last Synced: 2024-10-29T06:41:28.245Z (2 months ago)
• Topics: mcmc, random, sampling
• Language: Rust
• Homepage:
• Size: 16.6 KB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
`metromc`

===

Markov chain Monte Carlo (MCMC) sampling using the _Independence Metropolis-Hastings_ algorithm with uniform transition kernel.

[![Crates.io](https://img.shields.io/crates/v/metromc?style=flat-square&logo=rust)](https://crates.io/crates/metromc)

[![docs.rs](https://img.shields.io/badge/docs.rs-metromc-blue?style=flat-square&logo=docs.rs)](https://docs.rs/metromc)

[![Build Status](https://img.shields.io/github/actions/workflow/status/obsidiandynamics/metromc/master.yml?branch=master&style=flat-square&logo=github)](https://github.com/obsidiandynamics/metromc/actions/workflows/master.yml)

Uses the [tinyrand](https://github.com/obsidiandynamics/tinyrand) RNG to sample at a rate of ~50M samples/sec.

Supports the following distributions:

* [Gaussian/Normal](https://en.wikipedia.org/wiki/Normal_distribution)

* [Gamma](https://en.wikipedia.org/wiki/Gamma_distribution)

* [Pareto](https://en.wikipedia.org/wiki/Pareto_distribution)

* [Beta](https://en.wikipedia.org/wiki/Beta_distribution)

It is easy to add more univariate distributions by supplying an implementation of a PDF or wrapping one from the excellent [statrs](https://crates.io/crates/statrs) crate.

# Example

Draw samples from the Gaussian distribution using MCMC.

```rust

use std::ops::RangeInclusive;

use tinyrand::Wyrand;

use metromc::gaussian::Gaussian;

use metromc::sampler::{Config, Sampler};

// sample from the Gaussian with µ=0.0 and σ=1.0, in the interval [-5.0, 5.0]

let sampler = Sampler::new(Config {

    rand: Wyrand::default(),

    dist: Gaussian::new(0.0, 1.0),

    range: -5.0..=5.0,

});

// take 1,000 samples after dropping the first 10

for sample in sampler.skip(10).take(1_000) {

    println!("{sample:.6}");

}

```