Edward: A library for probabilistic modeling, inference, and criticism
Computation
2017-02-02 v3 Artificial Intelligence
Programming Languages
Applications
Machine Learning
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.
Cite
@article{arxiv.1610.09787,
title = {Edward: A library for probabilistic modeling, inference, and criticism},
author = {Dustin Tran and Alp Kucukelbir and Adji B. Dieng and Maja Rudolph and Dawen Liang and David M. Blei},
journal= {arXiv preprint arXiv:1610.09787},
year = {2017}
}