论文标题
Tensorbnn：使用TensorFlow推断神经网络的贝叶斯推断
TensorBNN: Bayesian Inference for Neural Networks using Tensorflow
论文作者
论文摘要
Tensorbnn是一个基于TensorFlow的新软件包，它对现代神经网络模型实现了贝叶斯推断。神经网络模型参数的后部密度表示为使用汉密尔顿蒙特卡洛采样的点云。 Tensorbnn软件包利用Tensorflow的架构和培训功能以及在培训和预测阶段使用现代图形处理单元（GPU）的能力。
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.
