Edward: A Library for Probabilistic Modeling, Inference, and Criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming.

References in zbMATH (referenced in 16 articles , 1 standard article )

Showing results 1 to 16 of 16.
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  1. Mathieu Besançon, Theodore Papamarkou, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, John Pearson: Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem (2021) not zbMATH
  2. Nemeth, Christopher; Fearnhead, Paul: Stochastic gradient Markov chain Monte Carlo (2021)
  3. Pourzanjani, Arya A.; Jiang, Richard M.; Mitchell, Brian; Atzberger, Paul J.; Petzold, Linda R.: Bayesian inference over the Stiefel manifold via the Givens representation (2021)
  4. Gurevich, Pavel; Stuke, Hannes: Dynamical systems approach to outlier robust deep neural networks for regression (2020)
  5. Lederman, Roy R.; Andén, Joakim; Singer, Amit: Hyper-molecules: on the representation and recovery of dynamical structures for applications in flexible macro-molecules in cryo-EM (2020)
  6. Pan, Shaowu; Duraisamy, Karthik: Physics-informed probabilistic learning of linear embeddings of nonlinear dynamics with guaranteed stability (2020)
  7. Zhang, Chelsea; Taylor, Sean J.; Cobb, Curtiss; Sekhon, Jasjeet: Active matrix factorization for surveys (2020)
  8. Baker, Jack; Fearnhead, Paul; Fox, Emily B.; Nemeth, Christopher: Control variates for stochastic gradient MCMC (2019)
  9. Kumar, R.; Colin, C.; Hartikainen, A.; Martin, O. A.: ArviZ a unified library for exploratory analysis of Bayesian models in Python. (2019) not zbMATH
  10. Wang, Yixin; Blei, David M.: The blessings of multiple causes (2019)
  11. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  12. Bach, Stephen H.; Broecheler, Matthias; Huang, Bert; Getoor, Lise: Hinge-loss Markov random fields and probabilistic soft logic (2017)
  13. Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth: sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo (2017) arXiv
  14. Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou: ZhuSuan: A Library for Bayesian Deep Learning (2017) arXiv
  15. Kucukelbir, Alp; Tran, Dustin; Ranganath, Rajesh; Gelman, Andrew; Blei, David M.: Automatic differentiation variational inference (2017)
  16. Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei: Edward: A library for probabilistic modeling, inference, and criticism (2016) arXiv