viabel: Variational Inference Approximation Bounds that are Efficient and Lightweight
This package computes bounds errors of the mean, standard deviation, and variances estimates produced by a continuous approximation to a (unnormalized) distribution. A canonical application is a variational approximation to a Bayesian posterior distribution. In particular, using samples from the approximation Q and evaluations of the (maybe unnormalized) log densities of Q and (target distribution) P, the package provides functionality to compute bounds on:
- the α-divergence between P and Q
- the p-Wasserstein distance between P and Q
- the differences between the means, standard deviations, and variances of P and Q
If you use this package, please cite:
Practical posterior error bounds from variational objectives. Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell, Tamara Broderick. arXiv:1910.04102 [stat.ML], 2019.
Compilation and testing
After cloning the repository, testing and installation is easy. To test the package:
pip install .