Markov Random Field
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This page contains resources about Markov Random Fields (undirected graphical models) or Markov Networks.
Contents
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 Understanding, 117(11), 1610-1627.
Tutorials[edit]
Software[edit]
See also[edit]
Other Resources[edit]
- Random Fields - Notebook