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zeus: Lightning Fast MCMC

Project description

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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.

GitHub arXiv Build Status License: GPL v3 Documentation Status

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 logp(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.rand(ndim)

sampler = zeus.sampler(logp, nwalkers, ndim, args=[ivar])
sampler.run(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


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