# Crate emcee [−] [src]

`emcee`

"The MCMC Hammer"

A re-implementation of `emcee`

in Rust. This library includes an implementation
of Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble
sampler. All credit for this crate belongs to Dan Foreman-Mackey, the original
author of `emcee`

## Attribution

If you make use of emcee in your work, please cite Dan's paper
(arXiv,
ADS,
`BibTeX`

).

## Basic usage

### Implementing models

The sampler requires a struct that implements `emcee::Prob`

, for example:

use emcee::{Guess, Prob}; struct Model; impl Prob for Model { fn lnlike(&self, params: &Guess) -> f64 { // Insert actual implementation here 0f64 } fn lnprior(&self, params: &Guess) -> f64 { // Insert actual implementation here 0f64 } }

The trait has a default implementation for `lnprob`

which computes the product
of the likelihood and prior probability (sum in log space) as per Bayes' rule. Invalid prior
values are marked by returning -`std::f64::INFINITY`

from the priors function.
Note your implementation is likely to need external data. This data should be included with
your `Model`

class, for example:

struct Model<'a> { x: &'a [f64], y: &'a [f64], } // Linear model y = m * x + c impl<'a> Prob for Model<'a> { fn lnlike(&self, params: &Guess) -> f64 { let m = params[0]; let c = params[1]; -0.5 * self.x.iter().zip(self.y) .map(|(xval, yval)| { let model = m * xval + c; let residual = (yval - model).powf(2.0); residual }).sum::<f64>() } fn lnprior(&self, params: &Guess) -> f64 { // unimformative priors 0.0f64 } }

### Initial guess

Next, construct an initial guess. A `Guess`

represents a proposal parameter
vector:

use emcee::Guess; let initial_guess = Guess::new(&[0.0f64, 0.0f64]);

The sampler implemented by this create uses multiple *walkers*, and as such the initial
guess must be replicated once per walker, and typically dispersed from the initial position
to aid exploration of the problem parameter space. This can be achieved with the
`create_initial_guess`

method:

let nwalkers = 100; let perturbed_guess = initial_guess.create_initial_guess(nwalkers); assert_eq!(perturbed_guess.len(), nwalkers);

### Constructing a sampler

The sampler generates new parameter vectors, assess the probability using a user-supplied probability model, accepts more likely parameter vectors and iterates for a number of iterations.

The sampler needs to know the number of walkers to use, which must be an even number
and at least twice the size of your parameter vector. It also needs the size of your
parameter vector, and your probability struct (which implements `Prob`

):

let nwalkers = 100; let ndim = 2; // m and c // Build a linear model y = m * x + c (see above) let initial_x = [0.0f64, 1.0f64, 2.0f64]; let initial_y = [5.0f64, 7.0f64, 9.0f64]; let model = Model { x: &initial_x, y: &initial_y, }; let sampler = emcee::EnsembleSampler::new(nwalkers, ndim, &model) .expect("could not create sampler");

Then run the sampler:

let niterations = 100; sampler.run_mcmc(&perturbed_guess, niterations).expect("error running sampler");

#### Iterative sampling

It is sometimes useful to get the internal values proposed and evaluated
during each proposal step of the sampler. In the Python version, the
method `sample`

is a generator which can be iterated over to evaluate
the sample steps.

In this Rust version, we provide this feature by exposing the
`sample`

method, which takes a callback, which is called
once per iteration with a single `Step`

object. For
example:

sampler.sample(&perturbed_guess, niterations, |step| { println!("Current iteration: {}", step.iteration); println!("Current guess vectors: {:?}", step.pos); println!("Current log posterior probabilities: {:?}", step.lnprob); });

### Studying the results

The samples are stored in the sampler's `flatchain`

which is constructed through the
`flatchain`

method on the sampler:

let flatchain = sampler.flatchain().unwrap(); for (i, guess) in flatchain.iter().enumerate() { // Skip possible "burn-in" phase if i < 50 * nwalkers { continue; } println!("Iteration {}; m={}, c={}", i, guess[0], guess[1]); }

## Modules

errors |
Errors |

## Structs

EnsembleSampler |
Affine-invariant Markov-chain Monte Carlo sampler |

Guess |
Represents an initial guess |

Step |
Struct representing the current iteration evaluation |

## Traits

Prob |
Encapsulate the model evaluation |