zeus: Lightning Fast MCMC
Project description
zeus is a pure-Python implementation of the Ensemble Slice Sampling method.
- Fast & Robust Bayesian Inference,
- Efficient Markov Chain Monte Carlo,
- No hand-tuning,
- Excellent performance in terms of autocorrelation time and convergence rate,
- Scale to multiple CPUs without any extra effort.
Example
For instance, if you wanted to draw samples from a 10-dimensional Gaussian, you would do something like:
import numpy as np
import zeus
def log_prob(x, ivar):
return - 0.5 * np.sum(ivar * x**2.0)
nsteps, nwalkers, ndim = 1000, 100, 10
ivar = 1.0 / np.random.rand(ndim)
start = np.random.randn(nwalkers,ndim)
sampler = zeus.sampler(nwalkers, ndim, log_prob, args=[ivar])
sampler.run_mcmc(start, nsteps)
Documentation
Read the docs at zeus-mcmc.readthedocs.io
Installation
To install zeus using pip run
pip install zeus-mcmc
Attribution
Please cite Karamanis & Beutler (2020) if you find this code useful in your research. The BibTeX entry for the paper is:
@article{zeus,
title={Ensemble Slice Sampling},
author={Minas Karamanis and Florian Beutler},
year={2020},
eprint={2002.06212},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
Licence
Copyright 2019-2020 Minas Karamanis and contributors.
zeus is free software made available under the GPL-3.0 License. For details see the LICENSE
file.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
zeus-mcmc-1.0.7.tar.gz
(9.7 kB
view hashes)
Built Distribution
zeus_mcmc-1.0.7-py3-none-any.whl
(22.1 kB
view hashes)
Close
Hashes for zeus_mcmc-1.0.7-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f85e187bbf329434746612389ea6979d91e13583c97433082b51cb9928d456fe |
|
MD5 | 21a8e6a09a556c1279e6fe52e14a1d64 |
|
BLAKE2b-256 | 8e1ae766842ef3371ec4169bc354c6f4ecbc9155c8e6cae6e2170756c9bd7878 |