… In passing, it is worth noting that there also exist generalizations of the concept of a vector …
sum of singular values of a matrix) can be construed as generalizations of the ℓ 2 - and the ℓ 1 -…

… of dozens to thousands of sparsely coupled switchable units. … subsystems by introducing
dynamic graphical models (DGMs). … DIMs can readily be generalized to more than 2 states per …

… a statistical graphical model for multivariate functional data, which are common in medical
applications such as EEG and fMRI. Recently published functional graphical models … sparse …

… determine or guide the choice of optimal sparse estimators. This … in the area of probabilistic
graphical models, thus an addi… were not exactly equivalent, subsequent generalizations have …

… graphical model can be efficiently estimated and provides a natural generalization of the
graphical … focus primarily on networks inferred using graphical modeling methods. For instance, …

… was later generalized to the perceptron model, inventing … sparsely connected factor graph
model. Let us explain this in detail as follows. The model can be represented by a factor graph […

… as a substantial generalization of these approaches: We … sparse directed acyclic graphical
models that subsumes many popular parametric, semiparametric, and nonparametric models …

… The Markov condition can be thought of as a generalization of a familiar principle in … Bear
in mind that the causal matrix we aim to estimate, B, is very sparse because it can be permuted …

… to derive sparse posterior summaries for global-local shrinkage priors. … graphical models.
Part IV is comprised of five chapters covering other approaches to Bayesian variable selection. …

… We exemplify our result in two tasks of interest in statistical learning: a) classification for a
mixture with sparse means, where we study the efficiency of l1 penalty with respect to l2; b) max…