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 aeson (>=1.5.6.0), base (>=4.7 && <5), bytestring (>=0.10.12.0), circular (>=0.4.0.0), containers (>=0.6.2.1), data-default (>=0.7.1.1), deepseq (>=1.4.4.0), directory (>=1.3.6.0), dirichlet (>=0.1.0.4), double-conversion (>=2.0.2.0), log-domain (>=0.13.1), microlens (>=0.4.12.0), monad-parallel (>=0.7.2.4), mwc-random (>=0.15.0.1), pretty-show (>=1.10), primitive (>=0.7.1.0), statistics (>=0.15.2.0), time (>=1.9.3), transformers (>=0.5.6.2), vector (>=0.12.3.0), zlib (>=0.6.2.3) [details]
License GPL-3.0-or-later
Copyright Dominik Schrempf (2021)
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 2021-06-12T15:57:24Z
Distributions LTSHaskell:0.8.2.0, NixOS:0.8.2.0, Stackage:0.8.2.0
Downloads 2356 total (67 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2021-06-12 [all 1 reports]

Readme for mcmc-0.5.0.0

[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.

Documentation

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

Examples

Example MCMC analyses can be built with Stack and are attached to this repository.

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

For example, estimate the accuracy of an archer with

stack exec archery

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).