搜索结果: 1-10 共查到“Gaussian Graphical Models”相关记录10条 . 查询时间(0.078 秒)
Node-Based Learning of Multiple Gaussian Graphical Models
graphical models structured sparsity alternating direction method of multipliers gene regulatory networks lasso multivariate normal
2013/4/28
We consider the problem of estimating high-dimensional Gaussian graphical models corresponding to a single set of variables under several distinct conditions. This problem is motivated by the task of ...
Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods
Distributed Learning Gaussian Graphical Models Marginal Likelihoods
2013/4/28
We consider distributed estimation of the inverse covariance matrix, also called the concentration matrix, in Gaussian graphical models. Traditional centralized estimation often requires iterative and...
TIGER: A Tuning-Insensitive Approach for Optimally Estimating Gaussian Graphical Models
TIGER Tuning-Insensitive Approach Optimally Estimating Gaussian Graphical Models
2012/11/22
We propose a new procedure for estimating high dimensional Gaussian graphical models. Our approach is asymptotically tuning-free and non-asymptotically tuning-insensitive: it requires very few efforts...
Composite likelihood estimation of sparse Gaussian graphical models with symmetry
Variable selection model selection penalized estimation Gaussian graphical model concentration matrix partial correlation matrix
2012/9/17
In this article, we discuss the composite likelihood estimation of sparse Gaussian graph-ical models. When there are symmetry constraints on the concentration matrix or partial correlation matrix, the...
Feedback Message Passing for Inference in Gaussian Graphical Models
Belief propagation feedback vertex set Gaussian graphical models graphs with cycles Markov random field
2011/6/17
While loopy belief propagation (LBP) performs reasonably well for inference in some Gaussian
graphical models with cycles, its performance is unsatisfactory for many others. In particular for some
m...
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...
Geometry of maximum likelihood estimation in Gaussian graphical models
Statistics Theory (math.ST) Algebraic Geometry (math.AG) Optimization and Control (math.OC)
2010/12/17
We study maximum likelihood estimation in Gaussian graphical models from a geometric point of view. An algebraic elimination criterion allows us to find exact lower bounds on the number of observation...
High-dimensional covariance estimation based on Gaussian graphical models
High-dimensional covariance estimation Gaussian graphical models
2010/12/15
Undirected graphs are often used to describe high dimensional distributions. Under sparsity
conditions, the graph can be estimated using ℓ1-penalization methods. We propose and study
the follo...
Inferring sparse Gaussian graphical models with latent structure
Gaussian graphical model Mixture model ℓ 1-penalization Model selection Variational inference EM algorithm
2009/9/16
Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional de...
Covariance estimation in decomposable Gaussian graphical models
Covariance estimation decomposable Gaussian graphical models
2010/3/18
Graphical models are a framework for representing and exploiting prior conditional independence structures within distributions using graphs. In the Gaussian case, these models are directly related to...