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
- 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
-
High-dimensional 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]
Tricks
-
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso, R. Mazumder and T. Hastie, 2011 [
block diagonal screening rule]
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)
Non gaussian graphical models
- Layer-wise learning strategy for nonparametric tensor product smoothing spline regression and graphical models, Tan et al. 2019
- On semiparametric exponential family graphical models, Yang et al. 2018
- Anomaly Detection and Localisation using Mixed Graphical Models, R. Laby and al, 2016
- Exponential series approaches for nonparametric graphical models, Janofsky 2015
- Learning structured densities via infinite dimensional exponen- tial families, Sun et al. 2015
- Graphical models via univariate exponential family distributions, Yang et al. 2015
- Selection and estimation for mixed graphical models, Chen et al. 2014
- Graph estimation with joint additive models, Voorman et al. 2014
- High-dimensional Ising model selection using l1-regularized logistic regression, Ravikumar et al. 2010
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
- Statistics for high-dimensional data, Buhlmann and Van De Geer 2011
