搜索结果: 1-15 共查到“理学 Graphical”相关记录26条 . 查询时间(0.093 秒)
Which graphical models are difficult to learn?
Ising model binary markov random field markov random
2015/8/21
We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their re...
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...
Robust control tools: graphical user-interfaces and LMI algorithms
Robust control graphics LMI algorithm control and numerical value the control design tools
2015/8/12
Gives some vague ideas about useful control design tools that might be based on LMI algorithms.
ASYMPTOTICS OF GRAPHICAL PROJECTION PURSUIT
Mathematical tools high-dimensional data planning and design
2015/7/14
Mathematical tools are developed for describing low-dimensional projec-
tions of high-dimensional data. Theorems are given to show that under
suitable conditions, most projections are approximatel...
Discussion of “Latent Variable Graphical Model Selection via Convex Optimization”
Latent Variable Graphical Model Selection Convex Optimization
2015/6/17
We wish to congratulate the authors for their innovative contribution, which is bound to inspire much further research. We find latent variable model selection to be a fantastic application of matrix ...
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...
Some graphical aspects of Frobenius structures
graphical aspects Frobenius structures Rings and Algebras
2012/3/1
We survey some aspects of Frobenius algebras, Frobenius structures and their relation to finite Hopf algebras using graphical calculus. We focus on the `yanking' moves coming from a closed structure i...
Finding Non-overlapping Clusters for Generalized Inference Over Graphical Models
Graphical Models Markov Random Fields Belief Propagation Loopy Belief Propagation Generalized Belief Propagation Block-Trees Block-Graphs
2011/10/9
Abstract: Graphical models compactly capture stochastic dependencies amongst a collection of random variables using a graph. Inference over graphical models corresponds to finding marginal probability...
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...
Geometry of maximum likelihood estimation in Gaussian graphical models
Geometry of maximum likelihood estimation Gaussian graphical models
2011/1/21
We study maximum likelihood estimation in Gaussian graphical models from a geometric point of view. An algebraic elimination criterion allows us to nd exact lower bounds on the number of observations...
Graphical Models Concepts in Compressed Sensing
Graphical Models Concepts Compressed Sensing
2010/11/23
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 s...