References in zbMATH (referenced in 28 articles )

Showing results 1 to 20 of 28.
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  1. Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann: From Things’ Modeling Language (ThingML) to Things’ Machine Learning (ThingML2) (2020) arXiv
  2. Chen, Wilson Y.; Wand, Matt P.: Factor graph fragmentization of expectation propagation (2020)
  3. Jan Luts; Shen Wang; John Ormerod; Matt Wand: Semiparametric Regression Analysis via Infer.NET (2018) not zbMATH
  4. Kim, Andy S. I.; Wand, Matt P.: On expectation propagation for generalised, linear and mixed models (2018)
  5. Chistikov, Dmitry; Dimitrova, Rayna; Majumdar, Rupak: Approximate counting in SMT and value estimation for probabilistic programs (2017)
  6. Culpepper, Ryan; Cobb, Andrew: Contextual equivalence for probabilistic programs with continuous random variables and scoring (2017)
  7. Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow: TerpreT: A Probabilistic Programming Language for Program Induction (2016) arXiv
  8. Huang, Daniel; Morrisett, Greg: An application of computable distributions to the semantics of probabilistic programming languages (2016)
  9. Kim, Andy S. I.; Wand, M. P.: The explicit form of expectation propagation for a simple statistical model (2016)
  10. Kiselyov, Oleg: Probabilistic programming language and its incremental evaluation (2016)
  11. Lee, Cathy Yuen Yi; Wand, Matt P.: Streamlined mean field variational Bayes for longitudinal and multilevel data analysis (2016)
  12. Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)
  13. Kamronn, Simon; Poulsen, Andreas Trier; Hansen, Lars Kai: Multiview Bayesian correlated component analysis (2015)
  14. Menictas, Marianne; Wand, Matt P.: Variational inference for heteroscedastic semiparametric regression (2015)
  15. Su, Hao; Yu, Adams Wei: Probabilistic modeling of scenes using object frames (2015) ioport
  16. Hoffman, Matthew D.; Gelman, Andrew: The no-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo (2014)
  17. Kim, Sungchul; Qin, Tao; Liu, Tie-Yan; Yu, Hwanjo: Advertiser-centric approach to understand user click behavior in sponsored search (2014) ioport
  18. Parson, Oliver; Ghosh, Siddhartha; Weal, Mark; Rogers, Alex: An unsupervised training method for non-intrusive appliance load monitoring (2014) ioport
  19. Bishop, Christopher M.: Model-based machine learning (2013)
  20. Borgström, Johannes; Gordon, Andrew D.; Greenberg, Michael; Margetson, James; Van Gael, Jurgen: Measure transformer semantics for Bayesian machine learning (2013)

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