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.
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-0.1.4.tar.gz
(53.2 kB
view details)
Built Distribution
mici-0.1.4-py3-none-any.whl
(52.4 kB
view details)
File details
Details for the file mici-0.1.4.tar.gz.
File metadata
- Download URL: mici-0.1.4.tar.gz
- Upload date:
- Size: 53.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.4.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 6633bee54a16d2e896848237eaa74ec6ebee69f8a5d69c69d669f4b3277a7313 |
|
| MD5 | 976f21ad2a3df9e1fd5eced8565809d0 |
|
| BLAKE2b-256 | cd9eebd1c482cca1ee28252ec86223edd007fd4853902fdf1a23f25118ee6aaf |
File details
Details for the file mici-0.1.4-py3-none-any.whl.
File metadata
- Download URL: mici-0.1.4-py3-none-any.whl
- Upload date:
- Size: 52.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.4.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d91b1c45e512b2c18846426e4a677b72d592a752770c0ac1d2cecfb53369bbc7 |
|
| MD5 | 3c615dcdc729fe49a4a6598f2ddeafd9 |
|
| BLAKE2b-256 | 85a186b9969e43a43e2c466bdbce4c8f704a486777dc84c69988abc47da117fb |
