# 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.

## Papers

### 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)

## R packages

### 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