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.

Other Resources

 * Random Fields - Notebook