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 (Geyer, Charles J, 2011);
- Metropolis-coupled Markov chain Monte Carlo (also known as parallel
tempering) (Geyer, Charles J, 1991, Altekar, Gautam and Dwarkadas, Sandhya and Huelsenbeck, John P and Ronquist, Fredrik, 2004);
- Hamilton Monte Carlo proposal (Neal, Radford M, 2011);
- No U-Turn Sampler (NUTS) (Matthew D. Hoffman and Andrew Gelman, 2014).
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.
References
Altekar, Gautam and Dwarkadas, Sandhya and Huelsenbeck, John P and Ronquist, Fredrik (2004). Parallel metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference.
Geyer, Charles J (2011). {Introduction to Markov Chain Monte Carlo}, CRC press.
Geyer, Charles J (1991). Markov chain Monte Carlo maximum likelihood.
Matthew D. Hoffman and Andrew Gelman (2014). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.
Neal, Radford M (2011). {MCMC Using Hamiltonian Dynamics}, CRC press.
