https://github.com/minaskar/zeus
⚡️ zeus: Lightning Fast MCMC ⚡️
https://github.com/minaskar/zeus
bayesian-inference black-box-bayesian-inference general-purpose inference machine-learning mcmc mcmc-sampler probabilistic-data-analysis python sampling sampling-methods
Last synced: about 2 months ago
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⚡️ zeus: Lightning Fast MCMC ⚡️
- Host: GitHub
- URL: https://github.com/minaskar/zeus
- Owner: minaskar
- License: gpl-3.0
- Created: 2019-10-25T02:14:32.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-02-18T17:26:24.000Z (over 1 year ago)
- Last Synced: 2025-03-28T17:08:30.176Z (about 2 months ago)
- Topics: bayesian-inference, black-box-bayesian-inference, general-purpose, inference, machine-learning, mcmc, mcmc-sampler, probabilistic-data-analysis, python, sampling, sampling-methods
- Language: Python
- Homepage: https://zeus-mcmc.readthedocs.io/
- Size: 197 MB
- Stars: 232
- Watchers: 8
- Forks: 33
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
**zeus is a Python implementation of the Ensemble Slice Sampling method.**
- Fast & Robust *Bayesian Inference*,
- Efficient *Markov Chain Monte Carlo (MCMC)*,
- Black-box inference, no hand-tuning,
- Excellent performance in terms of autocorrelation time and convergence rate,
- Scale to multiple CPUs without any extra effort,
- Automated Convergence diagnostics.
[](https://github.com/minaskar/zeus)
[](https://arxiv.org/abs/2002.06212)
[](https://arxiv.org/abs/2105.03468)
[](https://ascl.net/2008.010)
[](https://travis-ci.com/minaskar/zeus)
[](https://github.com/minaskar/zeus/blob/master/LICENSE)
[](https://zeus-mcmc.readthedocs.io/en/latest/?badge=latest)
[](https://pepy.tech/project/zeus-mcmc)
## Example
For instance, if you wanted to draw samples from a 10-dimensional Gaussian, you would do something like:
```python
import zeus
import numpy as np
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.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar])
sampler.run_mcmc(start, nsteps)
chain = sampler.get_chain(flat=True)
```
## Documentation
Read the docs at [zeus-mcmc.readthedocs.io](https://zeus-mcmc.readthedocs.io)
## Installation
To install ``zeus`` using ``pip`` run:
```bash
pip install zeus-mcmc
```
To install ``zeus`` in a [[Ana]Conda](https://conda.io/projects/conda/en/latest/index.html) environment use:
```bash
conda install -c conda-forge zeus-mcmc
```
## Attribution
Please cite the following papers if you found this code useful in your research:
```bash
@article{karamanis2021zeus,
title={zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference},
author={Karamanis, Minas and Beutler, Florian and Peacock, John A},
journal={arXiv preprint arXiv:2105.03468},
year={2021}
}
@article{karamanis2020ensemble,
title = {Ensemble slice sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions},
author = {Karamanis, Minas and Beutler, Florian},
journal = {arXiv preprint arXiv: 2002.06212},
year = {2020}
}
```
## Licence
Copyright 2019-2021 Minas Karamanis and contributors.
zeus is free software made available under the GPL-3.0 License. For details see the `LICENSE` file.