https://github.com/igutierrezm/walker2014sampling.jl

Sampling from an unnormalized pmf in Julia using Walker's MCMC algorithm.
https://github.com/igutierrezm/walker2014sampling.jl

julia mcmc walker-algorithm

Last synced: 25 days ago
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Sampling from an unnormalized pmf in Julia using Walker's MCMC algorithm.

• Host: GitHub
• URL: https://github.com/igutierrezm/walker2014sampling.jl
• Owner: igutierrezm
• License: mit
• Created: 2021-04-11T23:42:17.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-05-17T19:56:53.000Z (over 3 years ago)
• Last Synced: 2024-11-07T01:29:04.632Z (2 months ago)
• Topics: julia, mcmc, walker-algorithm
• Language: Julia
• Homepage:
• Size: 142 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Walker2014Sampling

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://igutierrezm.github.io/Walker2014Sampling.jl/stable)

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[![codecov](https://codecov.io/gh/igutierrezm/Walker2014Sampling.jl/branch/main/graph/badge.svg?token=o8DGQSTKft)](https://codecov.io/gh/igutierrezm/Walker2014Sampling.jl)

Sampling from an unnormalized [pmf](https://en.wikipedia.org/wiki/Probability_mass_function) in Julia using Walker's MCMC algorithm, see Walker (2014) for details.

## Installation

Install with the Julia package manager Pkg, just like any other registered Julia package:

```julia

# Press ']' to enter the Pkg REPL mode.

pkg> add Walker2014Sampling

```

or

```julia

julia> using Pkg; 

julia> Pkg.add("Walker2014Sampling")

```

## Usage

Suppose we want to draw the next state in a [MCMC](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo) with target (unnormalized) pmf `p()` using Walker's algorithm with parameter `k`. Suppose further that the support of `p()` is ℕ and the current state is `x0`.

The first step is to set up the environment:

```julia

using Random, Walker2014Sampling

rng = MersenneTwister(1)

```

Then, we create a sampler `s` using `Walker2014Sampler()`:

```julia

s = Walker2014Sampler(k);

``` 

Finally, we draw the next state using `rand()`:

```julia

x1 = rand(rng, p, s, x0);

``` 

Be aware that both `rng` and `s` are modified in the process. 

## References

Walker, S. G. (2014). Sampling unnormalized probabilities: An alternative to the Metropolis--Hastings algorithm. *SIAM Journal on Scientific Computing*, 36(2), A482–A494. https://doi.org/10.1137/130922549#