https://github.com/mauro3/KissMCMC.jl

Keep it simple, stupid, MCMC
https://github.com/mauro3/KissMCMC.jl

emcee mcmc-sampler

Last synced: 3 months ago
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Keep it simple, stupid, MCMC

• Host: GitHub
• URL: https://github.com/mauro3/KissMCMC.jl
• Owner: mauro3
• License: other
• Created: 2016-07-12T14:18:19.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2022-04-13T13:52:22.000Z (over 2 years ago)
• Last Synced: 2024-07-10T05:17:34.789Z (4 months ago)
• Topics: emcee, mcmc-sampler
• Language: Julia
• Size: 4.84 MB
• Stars: 14
• Watchers: 4
• Forks: 3
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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• awesome-sciml - mauro3/KissMCMC.jl: Keep it simple, stupid, MCMC

README

        
# KissMCMC

[![Build Status](https://github.com/mauro3/KissMCMC.jl/workflows/CI/badge.svg)](https://github.com/mauro3/KissMCMC.jl/actions)

[![Build Status](https://ci.appveyor.com/api/projects/status/github/mauro3/KissMCMC.jl?svg=true)](https://ci.appveyor.com/project/mauro3/KissMCMC-jl)

[![Coverage](https://codecov.io/gh/mauro3/KissMCMC.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/mauro3/KissMCMC.jl)

Got a probability density function you want to draw samples from?

Don't want to learn all the fancy stuff of the fancy sampler packages?

The KissMCMC (Keep it simple, stupid, MCMC) package intends to provide

a few simple MCMC samplers.

```julia

using KissMCMC

# the distribution to sample from,

logpdf(x::T) where {T} = x<0 ? -convert(T,Inf) : -x

# initial point of walker

theta0 = 0.5

# Metropolis MCMC sampler:

sample_prop_normal(theta) = 1.5*randn() + theta # samples the proposal (or jump) distribution

thetas, accept_ratio = metropolis(logpdf, sample_prop_normal, theta0, niter=10^5)

println("Accept ratio Metropolis: $accept_ratio")

# emcee MCMC sampler:

thetase, accept_ratioe = emcee(logpdf, make_theta0s(theta0, 0.1, logpdf, 100), niter=10^5)

# check convergence using integrated autocorrelation

thetase, accept_ratioe = squash_walkers(thetase, accept_ratioe) # puts all walkers into one

println("Accept ratio emcee: $accept_ratio")

using Plots

histogram(thetas, normalize=true, fillalpha=0.4)

histogram!(thetase, normalize=true, fillalpha=0.1)

plot!(0:0.01:5, map(x->exp(logpdf(x)[1]), 0:0.01:5), lw=3)

```

outputs:

![](https://cloud.githubusercontent.com/assets/4098145/16770344/dcb4a47a-484c-11e6-8f6e-0c2d223e9443.png)

MCMC samplers:

- Metropolis (serial) `metropolis`

- Affine invariant MCMC, aka emcee `emcee` (threaded)

# References

Other, probably better Julia MCMC packages:

- https://github.com/tpapp/DynamicHMC.jl

- https://github.com/madsjulia/AffineInvariantMCMC.jl

- https://github.com/brian-j-smith/Mamba.jl

- and many others

The (original) emcee python package: https://github.com/dfm/emcee