https://github.com/fmagrini/bayes-bay

Generalised Bayesian inversion framework
https://github.com/fmagrini/bayes-bay

bayesian-inference geophysical-inversions markov-chain-monte-carlo python trans-dimensional

Last synced: 3 months ago
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Generalised Bayesian inversion framework

• Host: GitHub
• URL: https://github.com/fmagrini/bayes-bay
• Owner: fmagrini
• License: bsd-2-clause
• Created: 2023-08-23T23:55:24.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-21T00:59:11.000Z (5 months ago)
• Last Synced: 2024-11-21T01:32:43.660Z (5 months ago)
• Topics: bayesian-inference, geophysical-inversions, markov-chain-monte-carlo, python, trans-dimensional
• Language: Python
• Homepage: https://bayes-bay.readthedocs.io
• Size: 43.7 MB
• Stars: 15
• Watchers: 2
• Forks: 2
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md

Awesome Lists containing this project

• awesome-seismology - BayesBay - Generalised trans-dimensional and hierarchical Bayesian inference.  (Inversion & Inference)

README

        
# BayesBay

[![PyPI version](https://img.shields.io/pypi/v/bayesbay?logo=pypi&style=flat-square&color=cae9ff&labelColor=f8f9fa)](https://pypi.org/project/bayesbay/)

[![Documentation Status](https://img.shields.io/readthedocs/bayes-bay?logo=readthedocs&style=flat-square&color=fed9b7&labelColor=f8f9fa&logoColor=eaac8b)](https://bayes-bay.readthedocs.io/en/latest/?badge=latest)

BayesBay is a user-friendly Python package designed for **generalised trans-dimensional and hierarchical Bayesian inference**. 

Optimised computationally through Cython, our library offers multi-processing capabilities and runs seamlessly on both standard computers and computer clusters. 

Distinguishing itself from existing packages, BayesBay provides **high-level functionalities for defining complex parameterizations**. 

These include prior probabilities that can be specified by uniform, Gaussian, or custom density functions and may vary depending on the spatial position in a 

hypothetical discretization. 

By default, BayesBay employs **reversible-jump Markov chain Monte Carlo** (MCMC) for sampling the posterior probability. 

It also offers options for **parallel tempering** or **simulated annealing**, while its low-level features enable the effortless implementation of arbitrary sampling criteria. 

Utilising object-oriented programming principles, BayesBay ensures that each component of an inference problem --- such as observed data, forward function(s), and parameterization --- 

is a self-contained unit. This design facilitates the integration of various forward solvers and data sets, promoting the simultaneous use of multiple data types in the 

considered inverse problem.

## Documentation

For comprehensive guides and examples on using BayesBay, make sure to check out our [documentation](https://bayes-bay.readthedocs.io/en/latest/).

## Development tips

- To set up development environment:

    ```console

    $ mamba env create -f envs/environment_dev.yml

    ```

    or 

    ```console

    $ python -m venv bayesbay_dev

    $ source bayesbay_dev/bin/activate

    $ pip install -r envs/requirements_dev.txt

    ```

- To install the package:

    ```console

    $ python -m pip install .

    ```

- Look at [noxfile.py](noxfile.py) for building, testing, formatting and linting.