  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 training features as well as its ability to use modern
graphics processing units (GPU) in both the training and prediction stages.
