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

The Git repository also includes example MCMC analyses. Build them with cabal-install or Stack.

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 a 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.