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}.

Documentation

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

Examples

Example MCMC analyses 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

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).