Resources on probabilistic graphical models

I started a Github repository resources-pgm where I will index some interesting resources on probabilistic graphical models. I will update it as I go along. For the moment, it is structured in books, articles and R packages. The content can also be found here.

Contents

Books

Papers

Gaussian graphical models

Review

Inference

Nodewise regression
Likelihood optimization

Fused graphical models

This section contains papers that do likelihood optimization or nodewise regression. The peculiarity is that the cost function includes a total variation term.

  • [Estimation of sparse Gaussian graphical models with hidden clustering structure], Lin et al. 2020 (preprint)
  • [Clustered Gaussian Graphical Model via Symmetric Convex clustering], Yao and Allen, 2019
  • [The joint graphical lasso for inverse covariance estimation across multiple classes], Danaher et al., 2014
  • [Local Neighborhood Fusion in Locally Constant Gaussian Graphical Models], Ganguly et al. 2014 (preprint)
Tricks

Mixed graphical models

R packages

Visualization

Inference

The CRAN Task View: gRaphical Models in R also lists a good number of packages on R linked to graphical models.

Optimization

  • [Optimization with sparsity-inducing penalties], Bach et al., 2011
  • [Convex Optimization], Boyd and Vandenberghe, 2004

Variable selection

  • [Statistical Learning with Sparsity], Hastie et al. 2016
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Edmond Sanou
PhD Student in Applied Mathematics