aemcmc 0.0.3

Features

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.

Getting started

TODO

Installation

The latest release of AeMCMC can be installed from PyPI using pip:

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:

pip install git+https://github.com/aesara-devs/aemcmc