Markov Random Field

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

Subfields and Concepts

• 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

Books and Book Chapters

• 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

• Wang, C., Komodakis, N., & Paragios, N. (2013). Markov random field modeling, inference & learning in computer vision & image understanding: A survey. Computer Vision and Image Understanding, 117(11), 1610-1627.

See also

• Markov Models
• Stochastic Processes and Random Fields
• Topology

Other Resources

• Random Fields - Notebook