Module documentation for 0.2.3
Markov chain Monte Carlo
Sample from a posterior using Markov chain Monte Carlo methods.
At the moment, the library is tailored to the Metropolis-Hastings algorithm since it covers most use cases. However, implementation of more algorithms is planned in the future.
The source code contains detailed documentation about general concepts as well as specific functions.
git clone https://github.com/dschrempf/mcmc.git cd mcmc stack build
For example, estimate the accuracy of an archer with
stack exec archery
Markov chain Monte Carlo sampling - ChangeLog
- Contrary proposals.
- Change how monitors are lifted (use normal function, not a lens).
- Remove concurrent monitors (was slow).
- Improve MCMC sampler output.
- Move away from hpack.
- Consistently use ByteString instead of Text.
- Verbosity levels.
- Improved handling of proposals, moves, and monitors.
- Bactrian moves.
- Many small changes.
Many changes; notably it is now possible to continue a Markov chain run.