Metadata-Version: 2.1
Name: viabel
Version: 0.5.0
Summary: Efficient, lightweight variational inference and approximation bounds
Home-page: https://github.com/jhuggins/viabel/
Author: Jonathan H. Huggins
Author-email: huggins@bu.edu
License: UNKNOWN
Description: #  VIABEL: *V*ariational *I*nference and *A*pproximation *B*ounds that are *E*fficient and *L*ightweight
        [![Build Status](https://travis-ci.org/jhuggins/viabel.svg?branch=master)](https://travis-ci.org/jhuggins/viabel) [![Code Coverage](https://codecov.io/gh/jhuggins/viabel/branch/master/graph/badge.svg)](https://codecov.io/gh/jhuggins/viabel) [![Documentation Status](https://readthedocs.org/projects/viabel/badge/?version=latest)](https://viabel.readthedocs.io/en/latest/?badge=latest)
        
        
        VIABEL is a library (still in early development) that provides two types of
        functionality:
        
        1. A lightweight, flexible set of methods for variational inference that is
        agnostic to how the model is constructed. All that is required is a
        log density and its gradient.
        2. Methods for computing bounds on the errors of the mean, standard deviation,
        and variance estimates produced by a continuous approximation to an
        (unnormalized) distribution. A canonical application is a variational
        approximation to a Bayesian posterior distribution.
        
        
        ## Documentation
        
        For examples and API documentation, see
        [readthedocs](https://viabel.readthedocs.io).
        
        ## Installation
        
        You can install the latest stable version using `pip install viabel`.
        Alternatively, you can clone the repository and use the master branch to
        get the most up-to-date version.
        
        ## Citing VIABEL
        
        If you use this package, please cite:
        
        [Validated Variational Inference via Practical Posterior Error Bounds](https://arxiv.org/abs/1910.04102).
        Jonathan H. Huggins,
        Miko&#0322;aj Kasprzak,
        Trevor Campbell,
        Tamara Broderick.
        In *Proc. of the 23rd International Conference on Artificial Intelligence and
        Statistics* (AISTATS), Palermo, Italy. PMLR: Volume 108, 2020.
        
        The equivalent BibTeX entry is:
        ```
        @inproceedings{Huggins:2020:VI,
          author = {Huggins, Jonathan H and Kasprzak, Miko{\l}aj and Campbell, Trevor and Broderick, Tamara},
          title = {{Validated Variational Inference via Practical Posterior Error Bounds}},
          booktitle = {Proc. of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS)},
          year = {2020}
        }
        ```
        
Platform: ALL
Classifier: Programming Language :: Python :: 3
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: dev
