PyMC3
Project description
Bayesian estimation, particularly using Markov chain Monte Carlo (MCMC), is an increasingly relevant approach to statistical estimation. However, few statistical software packages implement MCMC samplers, and they are non-trivial to code by hand. pymc3 is a python package that implements the Metropolis-Hastings algorithm as a python class, and is extremely flexible and applicable to a large suite of problems. pymc3 includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.
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
pymc3-3.0rc6.tar.gz
(992.5 kB
view details)
Built Distribution
pymc3-3.0rc6-py3-none-any.whl
(174.0 kB
view details)
File details
Details for the file pymc3-3.0rc6.tar.gz.
File metadata
- Download URL: pymc3-3.0rc6.tar.gz
- Upload date:
- Size: 992.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 8df5a190218d3eea2655915847aad9c87ae9719cc507c5c3e24ec4973408051d |
|
| MD5 | fc3bb36fbbf787053cf99147164b7439 |
|
| BLAKE2b-256 | d288355f4bde0eb4eadedd3b58d512a0994e5abd3b76ac467e5f7f174e9b61f7 |
Provenance
File details
Details for the file pymc3-3.0rc6-py3-none-any.whl.
File metadata
- Download URL: pymc3-3.0rc6-py3-none-any.whl
- Upload date:
- Size: 174.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 6117fecee091a72c267abf0f3b28f870f0edfbb7cbafa318c8f3dd70e2906fcd |
|
| MD5 | 6b5ac4d8be441b2899600ffb91d9bc61 |
|
| BLAKE2b-256 | 0b4fa2a4934a95fc78884b56ca507448aeb1b5344d078895a09ccfff31f11319 |
