We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance...

Previously, we proposed a long range mean field (LRMF) method to simplify the Hamiltonian of a one-dimensional (1D) chain with 1/r Coulomb repulsive interactions to study the vacancy ordering transitions in quasi-1D materials such as KCu7−xS4. We used a self-consistent (SC) method that...