Markov Random Field

From Ioannis Kourouklides

This page contains resources about Markov Random Fields (undirected graphical models) or Markov Networks.

Subfields and Concepts[edit]

  • Gibbs Random Field
  • Gaussian MRF / Undirected Gaussian Graphical Model / Multivariate Gaussian distribution (not to be confused with Gaussian Random Field)
  • Ising Model
    • Boolean Network (an example of Sequential Dynamical System)
    • Lattice Model
    • Potts Model
    • Hopfield Network
    • Boltzmann Machine
      • Restricted Boltzmann Machine
  • Conditional Random Field
  • Structural Support Vector Machine
  • Deep Boltzmann Machine
  • Associative Markov Network
  • Maximum Entropy (Maxent) Model
  • Structural Support Vector Machine (SSVM) / Max Margin Markov Network (M3net)
  • Factor Graph

Online Courses[edit]

Video Lectures[edit]

Lecture Notes[edit]

Books and Book Chapters[edit]

  • Theodoridis, S. (2015). "Section 15.4: Undirected Graphical Models". Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
  • Blake, A., Kohli, P., & Rother, C. (2011). Markov Random Fields for Vision and Image Processing. MIT Press.
  • Bishop, C. M. (2006). "Section 8.3: Markov Random Fields". Pattern Recognition and Machine Learning. Springer.
  • Rue, H., & Held, L. (2005). Gaussian Markov Random Fields: Theory and Applications. CRC Press.
  • Li, S. Z. (1995). Markov Random Field Modeling in Computer Vision. Springer Science & Business Media.
  • Chellappa, R., & Jain, A. (1993). Markov Random Fields: Theory and Applications. Academic Press.

Scholarly Articles[edit]

  • Wang, C., Komodakis, N., & Paragios, N. (2013). Markov random field modeling, inference & learning in computer vision & image understanding: A survey. Computer Vision and Image Understanding117(11), 1610-1627.



See also[edit]

Other Resources[edit]