搜索结果: 1-7 共查到“数理统计学 Graphical”相关记录7条 . 查询时间(0.062 秒)
The graphical lasso:New insights and alternatives
Graphical lasso sparse inverse covariance selection precision matrix convex analysis/optimization positive definite matrices sparsity semidefinite programming
2015/8/21
The graphical lasso [5] is an algorithm for learning the structure in an undirected Gaussian graphical model, using ℓ1 regularization to control the number of zeros in the precision matrix Θ = Σ...
Applications of the lasso and grouped lasso to the estimation of sparse graphical models
lasso and grouped lasso sparse graphical models
2015/8/21
We propose several methods for estimating edge-sparse and nodesparse graphical models based on lasso and grouped lasso penalties.We develop efficient algorithms for fitting these models when the numbe...
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso
sparse inverse covariance selection sparsity graphical lasso Gaussian graphical models graph connected components concentration graph large scale covariance estimation
2015/8/21
We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter λ. Suppose the sample covariance graph formed by thresholding the entries of the sampl...
Graphical Models Concepts in Compressed Sensing
Creative graphics model transfer the algorithm the compressed sensing the analysis of high-dimensional lasso risk limits
2015/8/20
This paper surveys recent work in applying ideas from graphical models and message passing algorithms to solve large scale regularized regression problems. In particular, the focus is on compressed se...
This paper studies the partial estimation of Gaussian graphical models from high-dimensional empirical observations. We derive a convex formulation for this problem using $\ell_1$-regularized maximum-...
Counterfactual Graphical Models for Mediation Analysis via Path-Specific Effects
Counterfactual Graphical Models Mediation Analysis Path-Specific Effects Statistics Theory
2012/5/24
Potential outcome counterfactuals represent variation in the outcome of interest after a hypothetical treatment or intervention is performed. Causal graphical models are a concise, intuitive way of re...
High-Dimensional Gaussian Graphical Model Selection: Tractable Graph Families
Gaussian graphical model selection high-dimensional learning local-separation property walk-summability
2011/9/29
Abstract: We consider the problem of high-dimensional Gaussian graphical model selection. We identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is based on...