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
  • Lattice Model
    • Potts Model
    • Ising 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 CoursesEdit

Video LecturesEdit

Lecture NotesEdit

Books and Book ChaptersEdit

  • 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 ArticlesEdit

  • 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 alsoEdit

Other ResourcesEdit

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