Gaussian Markov random fields (MRFs) are MRFs where the variables are all jointly Gaussian. The graph structure is reflected in the sparsity pattern of the precision matrix.
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Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)
Supplemental resources (the following are optional, but you may find them useful)
→ Probabilistic Graphical Models: Principles and Techniques
A very comprehensive textbook for a graduate-level course on probabilistic AI.
Location: Section 7.3, pages 254-257
- We often want to do one of the following in Gaussian MRFs:Gaussian Bayes nets , and the structure shows up in the Cholesky factorization of the covariance matrix.
- Important special cases includeuseful in low-level image processing .
- Many algorithms which are inefficient or inexact for general MRFs are efficient and exact for Gaussian MRFs. Examples include:
- inferring the mean with loopy belief propagation
- "learning the parameters:gaussian_mrf_parameter_learning
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