mcmc: Sample from a posterior using Markov chain Monte Carlo

[ gpl, library, math, statistics ] [ Propose Tags ]

Please see the README on GitHub at

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Versions [faq] 0.1.3, 0.2.0, 0.2.1
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Dependencies aeson, base (>=4.7 && <5), bytestring, containers, data-default, directory, double-conversion, log-domain, microlens, mwc-random, statistics, time, transformers, vector, zlib [details]
License GPL-3.0-or-later
Copyright Dominik Schrempf (2020)
Author Dominik Schrempf
Category Math, Statistics
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Source repo head: git clone
Uploaded by dschrempf at 2020-08-03T12:40:40Z
Distributions NixOS:0.1.3
Downloads 119 total (87 in the last 30 days)
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Status Hackage Matrix CI
Docs available [build log]
Last success reported on 2020-08-03 [all 1 reports]


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Maintainer's Corner

For package maintainers and hackage trustees

Readme for mcmc-0.2.1

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Markov chain Monte Carlo

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Sample from a posterior using Markov chain Monte Carlo methods.

At the moment, the library is tailored to the Metropolis-Hastings algorithm since it covers most use cases. However, implementation of more algorithms is planned in the future.


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


Have a look at the example MCMC analyses. They can be built with Stack and are attached to this repository.

git clone
cd mcmc
stack build

For example, estimate the accuracy of an archer with

stack exec archery