AeMCMC is a Python library that automates the construction of samplers for Aesara graphs that represent statistical models.
This project is currently in an alpha state, but the basic features/objectives are currently as follows:
- Provide utilities that simplify the process of constructing Aesara graphs/functions for posterior and posterior predictive sampling
- Host a wide array of “exact” posterior sampling steps (e.g. Gibbs steps, scale-mixture/decomposition-based conditional samplers, etc.)
- Build a framework for identifying and composing said sampler steps and enumerating the possible samplers for an arbitrary model
Overall, we would like this project to serve as a hub for community-sourced specialized samplers and facilitate their general use.
The latest release of AeMCMC can be installed from PyPI using
pip install aemcmc
Or via conda-forge:
conda install -c conda-forge aemcmc
The current development branch of AeMCMC can be installed from GitHub, also using
pip install git+https://github.com/aesara-devs/aemcmc