bayesbay 0.2.2

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BayesBay

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.

Development tips

• To set up development environment:

  $ mamba env create -f envs/environment_dev.yml
  

  or

  $ python -m venv bayesbay_dev
  $ source bayesbay_dev/bin/activate
  $ pip install -r envs/requirements_dev.txt
  

• To install the package:

  $ python -m pip install .
  

• Look at noxfile.py for building, testing, formatting and linting.