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mcmc: Sample from a posterior using Markov chain Monte Carlo

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Versions [RSS] 0.1.3, 0.2.0, 0.2.1, 0.2.2, 0.2.3, 0.2.4, 0.3.0, 0.4.0.0, 0.5.0.0, 0.6.0.0, 0.6.1.0, 0.6.2.0, 0.6.2.2, 0.6.2.3, 0.6.2.4, 0.6.2.5, 0.7.0.0, 0.7.0.1, 0.8.0.0, 0.8.0.1, 0.8.1.0, 0.8.2.0, 0.8.3.0, 0.8.3.1
Change log ChangeLog.md
Dependencies ad, aeson, async, base (>=4.7 && <5), bytestring, circular, containers, covariance (>=0.2), directory, dirichlet, hmatrix, log-domain, math-functions, microlens, mwc-random, parallel, primitive, random, splitmix, statistics, time, transformers, vector, zlib [details]
License GPL-3.0-or-later
Copyright 2021 Dominik Schrempf
Author Dominik Schrempf
Maintainer dominik.schrempf@gmail.com
Category Math, Statistics
Home page https://github.com/dschrempf/mcmc#readme
Bug tracker https://github.com/dschrempf/mcmc/issues
Source repo head: git clone https://github.com/dschrempf/mcmc
Uploaded by dschrempf at 2024-10-07T04:43:06Z
Distributions LTSHaskell:0.8.3.1, NixOS:0.8.3.1, Stackage:0.8.3.1
Downloads 3144 total (53 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2024-10-07 [all 1 reports]

Readme for mcmc-0.8.3.1

[back to package description]

Markov chain Monte Carlo sampler

Sample from a posterior using Markov chain Monte Carlo (MCMC) algorithms.

At the moment, the following algorithms are available:

  • Metropolis-Hastings-Green (Geyer, Charles J, 2011);
  • Metropolis-coupled Markov chain Monte Carlo (also known as parallel tempering) (Geyer, Charles J, 1991, Altekar, Gautam and Dwarkadas, Sandhya and Huelsenbeck, John P and Ronquist, Fredrik, 2004);
  • Hamilton Monte Carlo proposal (Neal, Radford M, 2011);
  • No U-Turn Sampler (NUTS) (Matthew D. Hoffman and Andrew Gelman, 2014).

Documentation

The source code contains detailed documentation about general concepts as well as specific functions.

Examples

The Git repository also includes example MCMC analyses. Build them with cabal-install or Stack.

git clone https://github.com/dschrempf/mcmc.git
cd mcmc
stack build

For example, estimate the accuracy of an archer with

stack exec archery

For a more involved example, have a look at a phylogenetic dating project.

References

Altekar, Gautam and Dwarkadas, Sandhya and Huelsenbeck, John P and Ronquist, Fredrik (2004). Parallel metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference.

Geyer, Charles J (2011). {Introduction to Markov Chain Monte Carlo}, CRC press.

Geyer, Charles J (1991). Markov chain Monte Carlo maximum likelihood.

Matthew D. Hoffman and Andrew Gelman (2014). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.

Neal, Radford M (2011). {MCMC Using Hamiltonian Dynamics}, CRC press.