LINFA: a Python library for variational inference with normalizing flow and annealing
This provides a tool for researchers and practitioners in statistics and machine learning, but it is incremental as it builds on existing variational inference methods.
The authors developed LINFA, a Python library for variational inference that handles computationally expensive models and difficult-to-sample distributions with dependent parameters, and they discuss its performance in benchmarks.
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.