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
Papers
Gaussian graphical models
Review
 Getting Started in Probabilistic Graphical Models, E. M. Airoldi, 2007

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
] 
Estimation of covariance and precision matrix, network structure and a view towards systems biology, M. Kuismin, M. Sillanpää, [
Review of methods and their associated R packages
]
Inference

Highdimensional graphs and variable selection with the Lasso, N. Meinshausen and P. Buhlmann, 2006, [
Neighborhood selection
] 
Sparse inverse covariance estimation with the graphical Lasso, J. Friedman and al., 2008, [
coordinate descent algorithm
] 
Exact Covariance Thresholding into Connected Components for LargeScale Graphical Lasso, R. Mazumder and T. Hastie, 2011 [
block diagonal screening rule
] 
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 $\lambda$.
]
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.