Edward: A library for probabilistic modeling, inference, and criticism

TL;DR

<3-5 sentence high-level summary>

Abstract

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.

Figures (15)

• Figure 1: Simulated data with a cosine relationship between $x$ and $y$.
• Figure 2: Posterior draws from the inferred Bayesian neural network.
• Figure 3: Box's loop.
• Figure 4: Random variables can be combined with other TensorFlow ops.
• Figure 5: Computational graph for a Beta-Bernoulli program.