… 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 -…

… efficient reconstruction algorithm for sparse pairwise binary graphical models with a near-…
Generalized regularized interaction screening estimator We propose a generalization of the …

… of predictors, the model is often assumed to be sparse, with only … is succinctly described by
a graphical model, where variables … , generalized linear models, Gaussian and non-Gaussian …

… graphical models with covariates (GGMx), a class of multivariate Gaussian distributions with
covariate-dependent sparse … We consider the following generalization of the prior density in (…

… 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 …

… 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 […

… 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…

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