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).
