# dyPolyChord¶

Nested sampling is a numerical method for Bayesian computation which simultaneously calculates posterior samples and an estimate of the Bayesian evidence for a given likelihood and prior. The approach is popular in scientific research, and performs well compared to Markov chain Monte Carlo (MCMC)-based sampling for multi-modal or degenerate posterior distributions.

dyPolyChord implements dynamic nested sampling using the efficient PolyChord sampler to provide state-of-the-art nested sampling performance. Any likelihoods and priors which work with PolyChord can be used (Python, C++ or Fortran), and the output files produced are in the PolyChord format.

To get started, see the installation instructions and the demo. N.B. dyPolyChord requires PolyChord v1.14 or higher.

For more details about dynamic nested sampling, see the dynamic nested sampling paper (Higson et al., 2019). For a discussion of dyPolyChord’s performance, see the performance section of the documentation.

If you use dyPolyChord in your academic research, please cite the two papers introducing the software and the dynamic nested sampling algorithm it uses (the BibTeX is below). Note that dyPolyChord runs use PolyChord, which also requires its associated papers to be cited.

@article{Higson2019dynamic,
author={Higson, Edward and Handley, Will and Hobson, Michael and Lasenby, Anthony},
title={Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation},
year={2019},
journal={Statistics and Computing},
doi={10.1007/s11222-018-9844-0},
url={https://doi.org/10.1007/s11222-018-9844-0},
archivePrefix={arXiv},
arxivId={1704.03459}}

@article{higson2018dypolychord,
title={dyPolyChord: dynamic nested sampling with PolyChord},
author={Higson, Edward},
year={2018},
journal={Journal of Open Source Software},
number={29},
pages={916},
volume={3},
doi={10.21105/joss.00965},
url={http://joss.theoj.org/papers/10.21105/joss.00965}}


## Changelog¶

The changelog for each release can be found at https://github.com/ejhigson/dyPolyChord/releases.

## Contributions¶

Contributions are welcome! Development takes place on github:

When creating a pull request, please try to make sure the tests pass and use numpy-style docstrings.

If you have any questions or suggestions please get in touch (e.higson@mrao.cam.ac.uk).