Mind maps are a great way to visually structure the main ideas of an article. I find this particularly useful when I browse more or less difficult articles. Here I share with you some of them. The content of a map is not exhaustive, it corresponds to my reading objective at a given time.
Papers Kuismin and Sillanpaa 2017 Kuismin and Sillanpaa 2017 Estimation of covariance and precision matrix, network structure and a view towards systems biology, M. Kuismin, M. Sillanpaa; 2017
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 Papers Gaussian graphical models Review Inference Non gaussian graphical models R packages Inference Optimization Variable selection 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)