Material Detail

Recent Advances in Bayesian Methods

Recent Advances in Bayesian Methods

This video was recorded at The 5th Asian Conference on Machine Learning (ACML), Canberra 2013. This year marks the 250th Anniversary of Bayes' theorem, which is playing an increasingly important role in statistical applications. Existing Bayesian models, especially nonparametric Bayesian methods, rely heavily on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors can affect posterior distributions through Bayes' theorem, recent work has shown that imposing posterior regularization is arguably more direct and in some cases can be more natural and easier. This tutorial will consist of two parts. First, I will review the recent developments of parametric and nonparametric Bayesian methods, with examples of Gaussian processes... Show More

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.