Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions. We developed LINFA (Library for Inference with Normalizing Flow and Annealing), a Python library for variational inference to accommodate computationally expensive models and difficult-to-sample distributions with dependent parameters. We discuss the theoretical background, capabilities, and performance of LINFA in various benchmarks. LINFA is publicly available on GitHub at https://github.com/desResLab/LINFA.
@article{arxiv.2307.04675,
title = {LINFA: a Python library for variational inference with normalizing flow and annealing},
author = {Yu Wang and Emma R. Cobian and Jubilee Lee and Fang Liu and Jonathan D. Hauenstein and Daniele E. Schiavazzi},
journal= {arXiv preprint arXiv:2307.04675},
year = {2023}
}