Edward

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 13 articles , 1 standard article )

Showing results 1 to 13 of 13.
Sorted by year (citations)

  1. Pan, Shaowu; Duraisamy, Karthik: Physics-informed probabilistic learning of linear embeddings of nonlinear dynamics with guaranteed stability (2020)
  2. Baker, Jack; Fearnhead, Paul; Fox, Emily B.; Nemeth, Christopher: Control variates for stochastic gradient MCMC (2019)
  3. Bingham, Eli; Chen, Jonathan P.; Jankowiak, Martin; Obermeyer, Fritz; Pradhan, Neeraj; Karaletsos, Theofanis; Singh, Rohit; Szerlip, Paul; Horsfall, Paul; Goodman, Noah D.: Pyro: deep universal probabilistic programming (2019)
  4. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  5. Kumar, R.; Colin, C.; Hartikainen, A.; Martin, O. A.: ArviZ a unified library for exploratory analysis of Bayesian models in Python. (2019) not zbMATH
  6. Wang, Yixin; Blei, David M.: The blessings of multiple causes (2019)
  7. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  8. Schreiber, Jacob: pomegranate: fast and flexible probabilistic modeling in Python (2018)
  9. Bach, Stephen H.; Broecheler, Matthias; Huang, Bert; Getoor, Lise: Hinge-loss Markov random fields and probabilistic soft logic (2017)
  10. Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth: sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo (2017) arXiv
  11. Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou: ZhuSuan: A Library for Bayesian Deep Learning (2017) arXiv
  12. Kucukelbir, Alp; Tran, Dustin; Ranganath, Rajesh; Gelman, Andrew; Blei, David M.: Automatic differentiation variational inference (2017)
  13. 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