The Python ensemble sampling toolkit for affine-invariant MCMC
Project description
The Python ensemble sampling toolkit for affine-invariant MCMC
emcee is a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the Astrophysics literature.
Documentation
Read the docs at emcee.readthedocs.io.
Attribution
Please cite Foreman-Mackey, Hogg, Lang & Goodman (2012) if you find this code useful in your research and add your paper to the testimonials list. The BibTeX entry for the paper is:
@article{emcee,
author = {{Foreman-Mackey}, D. and {Hogg}, D.~W. and {Lang}, D. and {Goodman}, J.},
title = {emcee: The MCMC Hammer},
journal = {PASP},
year = 2013,
volume = 125,
pages = {306-312},
eprint = {1202.3665},
doi = {10.1086/670067}
}
License
Copyright 2010-2017 Dan Foreman-Mackey and contributors.
emcee is free software made available under the MIT License. For details see the LICENSE file.
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
Built Distribution
File details
Details for the file emcee-3.0.0.tar.gz.
File metadata
- Download URL: emcee-3.0.0.tar.gz
- Upload date:
- Size: 26.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 353c26d8a8b09553532cd93662ddbedcd1a77feecefda5e46ea7e38829dede89 |
|
| MD5 | 4424318732c500a1d4db1b71e3eea7cf |
|
| BLAKE2b-256 | 1701ba037fec0c3ae92145a62ed62c95e9d99e1a7c45ce43ac3830dcf86eee88 |
File details
Details for the file emcee-3.0.0-py2.py3-none-any.whl.
File metadata
- Download URL: emcee-3.0.0-py2.py3-none-any.whl
- Upload date:
- Size: 51.9 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | db451e4a95c15f7828d4ad0af1b0809b54cf067bacc8b827112b9ab40ce6bc31 |
|
| MD5 | 8fcb85fed4c6a37c5a36c653d9883312 |
|
| BLAKE2b-256 | 00080d86d0af356d50ff00d741c34a90a971e7ae0a0aeb2bd050bcacfdc34229 |
