TensorBNN: Bayesian inference for neural networks using TensorFlow

Braden Kronheim, Michelle P. Kuchera, Harrison B. Prosper

Abstract: TensorBNN is a new package based on TensorFlow that implements Bayesian inference for modern neural network models. The posterior density of neural network model parameters is represented as a point cloud sampled using Hamiltonian Monte Carlo. The TensorBNN package leverages TensorFlow's architecture and its ability to use modern graphics processing units in both the training and prediction stages.