Metadata-Version: 2.1
Name: zeus-mcmc
Version: 1.2.0
Summary: zeus: Lightning Fast MCMC
Home-page: https://github.com/minaskar/zeus
Author: Minas Karamanis
Author-email: minaskar@gmail.com
License: UNKNOWN
Description: ![logo](logo.png)
        
        **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,
        - Included *Convergence Diagnostics*.
        
        [![GitHub](https://img.shields.io/badge/GitHub-minaskar%2Fzeus-blue)](https://github.com/minaskar/zeus)
        [![arXiv](https://img.shields.io/badge/arXiv-2002.06212-red)](https://arxiv.org/abs/2002.06212)
        [![Build Status](https://travis-ci.com/minaskar/zeus.svg?token=xnVWRZ3TFg1zxQYQyLs4&branch=master)](https://travis-ci.com/minaskar/zeus)
        [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/minaskar/zeus/blob/master/LICENSE)
        [![Documentation Status](https://readthedocs.org/projects/zeus-mcmc/badge/?version=latest&token=4455dbf495c5a4eaba52de26ac56628aad85eb3eadc90badfd1703d0a819a0f9)](https://zeus-mcmc.readthedocs.io/en/latest/?badge=latest)
        
        
        ## Example
        
        For instance, if you wanted to draw samples from a 10-dimensional Gaussian, you would do something like:
        
        ```python
        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](https://zeus-mcmc.readthedocs.io)
        
        
        ## Installation
        
        To install zeus using pip run
        
        ```bash
        pip install zeus-mcmc
        ```
        
        ## Attribution
        
        Please cite [Karamanis & Beutler (2020)](https://arxiv.org/abs/2002.06212) if you find this code useful in your
        research. The BibTeX entry for the paper is:
        
        ```bash
        @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.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
