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, async, base (>=4.7 && <5), bytestring, circular, containers, covariance (>=0.2), data-default, deepseq, directory, dirichlet, double-conversion, hmatrix, log-domain, math-functions, microlens, mwc-random, pretty-show, primitive, statistics, time, transformers, vector, zlib [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 2022-03-29T11:59:47Z
Distributions LTSHaskell:0.8.2.0, NixOS:0.8.2.0, Stackage:0.8.2.0
Downloads 2345 total (77 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2022-03-29 [all 1 reports]

Readme for mcmc-0.6.2.4

[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

Example MCMC analyses can be built with cabal-install or 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

For a more involved example, have a look at the 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.