Математические методы прогнозирования (практика, В.В. Стрижов)/Группа 574, осень 2019

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Версия от 21:42, 26 августа 2019; Strijov (Обсуждение | вклад)
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This series of seminars continues the course Bayesian model selection and investigates the theoretical aspects of model selection in various application problems.

  • Seminar 1 (Isachenko)
    • Plate notation and Bayesian inference in examples
    • Variational inference
    • Variational autoencoder
    • ELBO
  • Seminar 2 (Bakhteev)
    • Methods of model selection
    • Generalization theorem
  • Seminar 3 (Bakhteev)
    • Complexity theorems
  • Seminar 4 (Grabovoy?)
    • Mixture of experts
    • Priors on the mixture
    • Privileged learning and distilling
  • Seminar 5 (Aduenko?)
    • Theorem on number of experts
  • Seminar 6 (Isachenko)
    • Generative versus discriminative
  • Seminar 7 (Isachenko)
    • Zoo of variational autoencoders
  • Seminar 8 (Isachenko)
    • GAN
  • Seminar 9 (Vladimirova?)
    • Prior propagation for deep learning networks
  • Seminar 10
    • Directional Bayesian statistics
  • Seminar 11
    • Bayesian structure learning
  • Seminar 12
    • Probabilistic metric space construction
  • Seminar 13
    • Informative prior
  • Seminar 14
    • Bayesian programming






  • Informative prior with applications
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