Hackage :: [Package]

mcmc: Sample from a posterior using Markov chain Monte Carlo

[ gpl, library, math, statistics ] [ Propose Tags ]
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
Change log ChangeLog.md
Dependencies ad, aeson, async, base (>=4.7 && <5), bytestring, circular, containers, covariance (>=0.2), data-default, directory, dirichlet, double-conversion, hmatrix, log-domain, math-functions, microlens, mwc-random, parallel, pretty-show, 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 2022-07-11T05:28:44Z
Distributions LTSHaskell:0.8.2.0, NixOS:0.8.2.0, Stackage:0.8.2.0
Downloads 2322 total (78 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2022-07-11 [all 1 reports]

Readme for mcmc-0.7.0.1

[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 1;
  • Metropolis-coupled Markov chain Monte Carlo (also known as parallel tempering) 2 , 3.
  • Hamilton Monte Carlo proposal 4.

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.

Footnotes

1 Geyer, C. J., Introduction to Markov chain Monte Carlo, In Handbook of Markov Chain Monte Carlo (pp. 45) (2011). CRC press.

2 Geyer, C. J., Markov chain monte carlo maximum likelihood, Computing Science and Statistics, Proceedings of the 23rd Symposium on the Interface, (1991).

3 Altekar, G., Dwarkadas, S., Huelsenbeck, J. P., & Ronquist, F., Parallel metropolis coupled markov chain monte carlo for bayesian phylogenetic inference, Bioinformatics, 20(3), 407–415 (2004).

4 Neal, R. M., Mcmc Using Hamiltonian Dynamics, In S. Brooks, A. Gelman, G. Jones, & X. Meng (Eds.), Handbook of Markov Chain Monte Carlo (2011). CRC press.