mlsanityData to 7 Oct 2026

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

arXiv 1610.09787 ↗First posted 31 Oct 2016stat.CO cs.AI cs.PLcited by 28

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Abstract (arXiv)

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.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Oct 2016.

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Citing papers per year: 2021: 17; 2022: 6; 2023: 2; 2024: 2; 2025: 1.

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  1. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  2. 2023World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challengescited by 35
  3. 2022NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operatorscited by 33
  4. 2021Local convexity of the TAP free energy and AMP convergence for Z2-synchronizationcited by 15
  5. 2021Causal Mediation Analysis with Hidden Confounderscited by 10
  6. 2021Empirical Evaluation of Biased Methods for Alpha Divergence Minimizationcited by 10
  7. 2021Automatic variational inference with cascading flowscited by 9
  8. 2021Breiman's two cultures: You don't have to choose sidescited by 8
  9. 2021Learning Proposals for Probabilistic Programs with Inference Combinatorscited by 6
  10. 2022Foundation Posteriors for Approximate Probabilistic Inferencecited by 6
  11. 2022Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inferencecited by 5
  12. 2021Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modelingcited by 3
16 more
  1. 2021BayesPerf: Minimizing Performance Monitoring Errors Using Bayesian Statisticscited by 2
  2. 2021Meta-Learning an Inference Algorithm for Probabilistic Programscited by 1
  3. 2021MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectoriescited by 1
  4. 2021Supervising the Decoder of Variational Autoencoders to Improve Scientific Utilitycited by 1
  5. 2022Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with Automungecited by 1
  6. 2025Prior-Guided Residual Diffusion: Calibrated and Efficient Medical Image Segmentationcited by 1
  7. 2021Variational Inference for Category Recommendation in E-Commerce platformscited by 0
  8. 2021Boosting Variational Inference With Locally Adaptive Step-Sizescited by 0
  9. 2021Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficientlycited by 0
  10. 2021Pixyz: a Python library for developing deep generative modelscited by 0
  11. 2021Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representationscited by 0
  12. 2022On Reinforcement Learning, Effect Handlers, and the State Monadcited by 0
  13. 2022Borch: A Deep Universal Probabilistic Programming Languagecited by 0
  14. 2023Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networkscited by 0
  15. 2024Probabilistic Answer Set Programming with Discrete and Continuous Random Variablescited by 0
  16. 2024Identifying latent disease factors differently expressed in patient subgroups using group factor analysiscited by 0