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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 aeson, base (>=4.7 && <5), bytestring, containers, data-default, directory, log-domain, microlens, mwc-random, statistics, text, time, transformers, vector, zlib [details]
License GPL-3.0-or-later
Copyright Dominik Schrempf (2020)
Author Dominik Schrempf
Maintainer dominik.schrempf@gmail.com
Uploaded by dschrempf at 2020-07-30T13:01:25Z
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
Distributions LTSHaskell:0.8.3.1, NixOS:0.8.3.1, Stackage:0.8.3.1
Downloads 3329 total (58 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2020-07-30 [all 1 reports]

Readme for mcmc-0.2.0

[back to package description]

Markov chain Monte Carlo

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