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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 [cite:@Geyer2011];
  • Metropolis-coupled Markov chain Monte Carlo (also known as parallel tempering) [cite:@Geyer1991; @Altekar2004];
  • Hamilton Monte Carlo proposal [cite:@Neal2011];
  • No U-Turn Sampler (NUTS) [cite:@Hoffman2014].

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.

Ideas

Marginal likelihood

E.g., stepping stone (see RevBayes).

Proposals on tree topologies.

  • NNI.
  • Narrow. What is this? See RevBayes.
  • FNPR (same here).

General questions: How do we handle changing topologies? Then, the node paths change, and everything is messed up.

References