harmonic is an open source, well tested and documented Python implementation of the learnt harmonic mean estimator (McEwen et al. 2021) to compute the marginal likelihood (Bayesian evidence), required for Bayesian model selection.
For an accessible overview of the learnt harmonic mean estimator please see this Towards Data Science article.
While harmonic requires only posterior samples, and so is agnostic to the technique used to perform Markov chain Monte Carlo (MCMC) sampling, harmonic works exceptionally well with MCMC sampling techniques that naturally provide samples from multiple chains by their ensemble nature, such as affine invariant ensemble samplers. We therefore advocate use of harmonic with the popular emcee code implementing the affine invariant sampler of Goodman & Weare (2010).
Basic usage is highlighted in this interactive demo.
Brief installation instructions are given below (for further details see the full installation documentation).
The harmonic package can be installed by running
pip install harmonicThe harmonic package can also be installed from source by running
git clone https://github.com/astro-informatics/harmonic
cd harmonicand running the install script, within the root directory, with one command
bash build_harmonic.shTo check the install has worked correctly run the unit tests with
pytestComprehensive documentation for harmonic is available.
Jason D. McEwen, Christopher G. R. Wallis, Matthew A. Price, Matthew M. Docherty, Alessio Spurio Mancini
Please cite McEwen et al. (2021) if this code package has been of use in your project.
A BibTeX entry for the paper is:
@article{harmonic,
author = {Jason~D.~McEwen and Christopher~G.~R.~Wallis and Matthew~A.~Price and Matthew~M.~Docherty},
title = {Machine learning assisted {B}ayesian model comparison: learnt harmonic mean estimator},
journal = {ArXiv},
eprint = {arXiv:2111.12720},
year = 2021
}
Please also cite Spurio Mancini et al. (2022) if this code has been of use in a simulation-based inference project.
A BibTeX entry for the paper is:
@article{harmonic_sbi,
author = {Spurio Mancini, A. and Docherty, M. M. and Price, M. A. and McEwen, J. D.},
title = {{B}ayesian model comparison for simulation-based inference},
journal = {ArXiv},
eprint = {arXiv:2207.04037},
year = 2022
}
harmonic is released under the GPL-3 license (see LICENSE.txt), subject to
the non-commercial use condition (see LICENSE_EXT.txt)
harmonic Copyright (C) 2021 Jason D. McEwen, Christopher G. R. Wallis, Matthew A. Price, Matthew M. Docherty, Alessio Spurio Mancini & contributors This program is released under the GPL-3 license (see LICENSE.txt), subject to a non-commercial use condition (see LICENSE_EXT.txt). This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.