mcmc: Sample from a posterior using Markov chain Monte Carlo

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

Please see the README on GitHub at https://github.com/dschrempf/mcmc#readme


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Versions [faq] 0.1.3, 0.2.0, 0.2.1
Change log ChangeLog.md
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
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 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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Readme for mcmc-0.2.1

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Markov chain Monte Carlo

<p align="center"><img src="https://travis-ci.org/dschrempf/mcmc.svg?branch=master"/></p>

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.

Documentation

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

Examples

Have a look at the example MCMC analyses. They 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