Distributed, likelihood-free ABC-SMC inference
Project description
pyABC
Massively parallel, distributed and scalable ABC-SMC (Approximate Bayesian Computation - Sequential Monte Carlo) for parameter estimation of complex stochastic models. Implemented in Python with support of the R language.
- Documentation: https://pyabc.readthedocs.io
- Contact: https://pyabc.readthedocs.io/en/latest/about.html
- Source: https://github.com/icb-dcm/pyabc
- Bug reports: https://github.com/icb-dcm/pyabc/issues
Examples
Many examples are available as Jupyter Notebooks in the examples directory and also for download and for online inspection in the example section of the documentation.
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 Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distribution
pyabc-0.9.6-py3-none-any.whl
(104.9 kB
view details)
File details
Details for the file pyabc-0.9.6-py3-none-any.whl.
File metadata
- Download URL: pyabc-0.9.6-py3-none-any.whl
- Upload date:
- Size: 104.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/40.2.0 requests-toolbelt/0.8.0 tqdm/4.25.0 CPython/3.6.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 62a5258e9be4f9ab868518372a6eaa5fc4da8208e2628a4e227c0dd1221985b2 |
|
| MD5 | a52a988040a06f9484c43a09aafd2a28 |
|
| BLAKE2b-256 | 0c1513463108e87f43d06ae5a62419bfad7c86af84b0481bb30acad35a44686f |
