Edward: A library for probabilistic modeling, inference, and criticism
This provides a tool for researchers and practitioners in machine learning to develop and evaluate probabilistic models more efficiently, though it is incremental as it builds on existing frameworks.
The authors introduced Edward, a library for probabilistic modeling, inference, and criticism, building on TensorFlow to support distributed training and hardware like GPUs, enabling complex models at massive scale.
Probabilistic modeling is a powerful approach for analyzing empirical information. We describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative process pioneered by George Box: build a model of a phenomenon, make inferences about the model given data, and criticize the model's fit to the data. Edward supports a broad class of probabilistic models, efficient algorithms for inference, and many techniques for model criticism. The library builds on top of TensorFlow to support distributed training and hardware such as GPUs. Edward enables the development of complex probabilistic models and their algorithms at a massive scale.