Resources on probabilistic graphical models
I started a Github repository
resourcespgm
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
 Probabilistic Graphical Models, D. Koller and N. Friedman, 2009
 Graphical models, S L Lauritzen, 1996
 Graphical Models with R, S. Højsgaard, D. Edwards, S. Lauritzen; 2012
 Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics, C. Sinoquet and R. Mourad, 2014
Papers
Gaussian graphical models
Review
 Structure Learning in Graphical Modeling, Drton and Maathuis 2017

Estimation of covariance and precision matrix, network structure and a view towards systems biology, M. Kuismin, M. Sillanpää 2017, [
Review of methods and their associated R packages
] 
An Overview on the Estimation of Large Covariance and Precision Matrices, J. Fan, Y. Liao, H. Liu; 2015, [
Review of covariance and precision matrices estimation
]  Getting Started in Probabilistic Graphical Models, E. M. Airoldi, 2007
Inference
Nodewise regression

Highdimensional graphs and variable selection with the Lasso, N. Meinshausen and P. Buhlmann, 2006, [
Neighborhood selection
]
Likelihood optimization
 [BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables], Hsieh et al. 2013

The cluster graphical Lasso for improved estimation of gaussian graphical models, K. M. Tan and al., 2013, [
The connected components returned by Graphical Lasso with regularization parameter $\lambda$ are the same that the clusters obtained through Single Linkage hierarchical clustering on empirical covariance with dendrogram cut at level $\lambda$.
] 
Sparse inverse covariance estimation with the graphical Lasso, J. Friedman and al., 2008, [
coordinate descent algorithm
]
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

Exact Covariance Thresholding into Connected Components for LargeScale Graphical Lasso, R. Mazumder and T. Hastie, 2011 [
block diagonal screening rule
]
Mixed graphical models
 Anomaly Detection and Localisation using Mixed Graphical Models, R. Laby and al, 2016
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 sparsityinducing penalties], Bach et al., 2011
 [Convex Optimization], Boyd and Vandenberghe, 2004
Variable selection
 [Statistical Learning with Sparsity], Hastie et al. 2016