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
- 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
-
High-dimensional 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 Large-Scale 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.
