Metadata-Version: 2.1
Name: aemcmc
Version: 0.0.4
Summary: Miscellaneous MCMC samplers written in Aesara
Home-page: http://github.com/aesara-devs/aemcmc
Maintainer: Brandon T. Willard
Maintainer-email: aesara-devs@gmail.com
License: UNKNOWN
Description: |Tests Status| |Coverage| |Gitter|
        
        AeMCMC is a Python library that automates the construction of samplers for `Aesara <https://github.com/pymc-devs/aesara>`_ graphs that represent statistical models.
        
        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
        
        
        
        .. |Tests Status| image:: https://github.com/aesara-devs/aemcmc/workflows/Tests/badge.svg
          :target: https://github.com/aesara-devs/aemcmc/actions?query=workflow%3ATests
        .. |Coverage| image:: https://codecov.io/gh/aesara-devs/aemcmc/branch/main/graph/badge.svg?token=45nKZ7fDG5
          :target: https://codecov.io/gh/aesara-devs/aemcmc
        .. |Gitter| image:: https://badges.gitter.im/aesara-devs/aesara.svg
          :target: https://gitter.im/aesara-devs/aesara?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
