# 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 ;
- Metropolis-coupled Markov chain Monte Carlo (also known as parallel
tempering) , .
- Hamilton Monte Carlo proposal .
## Documentation
The [source code](https://hackage.haskell.org/package/mcmc) contains detailed documentation about general concepts as well
as specific functions.
## Examples
[Example MCMC analyses](https://github.com/dschrempf/mcmc/tree/master/mcmc-examples) can be built with [cabal-install](https://cabal.readthedocs.io/en/latest/cabal-commands.html#) or [Stack](https://docs.haskellstack.org/en/stable/README/) 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](https://github.com/dschrempf/mcmc/blob/master/mcmc-examples/Archery/Archery.hs) with
stack exec archery
For a more involved example, have a look at the [phylogenetic dating project](https://github.com/dschrempf/mcmc-dating).
# 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.