MCMC samplers based on simulating Hamiltonian dynamics on a manifold
Project description
Mici is a Python package providing implementations of Markov chain Monte Carlo (MCMC) methods for approximate inference in probabilistic models, with a particular focus on MCMC methods based on simulating Hamiltonian dynamics on a manifold.
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
mici-0.1.5.tar.gz
(68.1 kB
view details)
Built Distribution
mici-0.1.5-py3-none-any.whl
(67.3 kB
view details)
File details
Details for the file mici-0.1.5.tar.gz
.
File metadata
- Download URL: mici-0.1.5.tar.gz
- Upload date:
- Size: 68.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 451bcce921f0ff2d772415771688ff79f6b81905823566e002a8a44c6c6e4b4b |
|
MD5 | 0912bf37232db7881eea441eb45afcde |
|
BLAKE2b-256 | bde8b3a4c601228df3f4ccd5b82033eb859c79fa4da444a890c753e6cab992f2 |
File details
Details for the file mici-0.1.5-py3-none-any.whl
.
File metadata
- Download URL: mici-0.1.5-py3-none-any.whl
- Upload date:
- Size: 67.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 10d82a125a1d2e39f41fb523c127e27656126d3cd6c3cfefa78bb82386398a87 |
|
MD5 | e5b2a8ce7cae141b7be03547216274e2 |
|
BLAKE2b-256 | 82abe98e3b5d297b3ed16287703274f8903344d55d2c168f84f3b20556c58b5b |