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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Multiclass Sparse Discriminant Analysis Incorporating Graphical Structure among Predictors
预测变量 图形结构 多类稀疏 判别分析
2023/5/9
In the era of big data, many sparse linear discriminant analysis methods have been proposed for the classification and variable selection of the high-dimensional data. In order to solve the multiclass...
Learning the Structure of Mixed Graphical Models
Learning the Structure Mixed Graphical Models
2015/8/21
We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and di...
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...
Concepts and a case study for a flexible class of graphical Markov models
Concepts a case study a flexible class graphical Markov models
2013/4/27
With graphical Markov models, one can investigate complex dependences, summarize some results of statistical analyses with graphs and use these graphs to understand implications of well-fitting models...
Fused Multiple Graphical Lasso
Fused Multiple Graphical Lasso
2012/11/23
In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating ...
Graphical methods for inequality constraints in marginalized DAGs
Graphical methods inequality constraints marginalized DAGs
2012/11/22
We present a graphical approach to deriving inequality constraints for directed acyclic graph (DAG) models, where some variables are unobserved. In particular we show that the observed distribution of...
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...
The Graphical Identification for Total Effects by using Surrogate Variables
Graphical Identification Total Effects Surrogate Variables
2012/9/19
Consider the case where cause-effect relation-ships between variables can be described as a directed acyclic graph and the corresponding linear structural equation model. This paper provides graphical...
ARMA Time-Series Modeling with Graphical Models
ARMA Time-Series Modeling Graphical Models
2012/9/19
We express the classic ARMA time-series model as a directed graphical model. In doing so, we find that the deterministic re-lationships in the model make it effectively impossible to use the EM algori...
TheMacaulay2packageGraphicalModelscontains algorithms for the algebraic study of graphical models associated to undirected, directed and mixed graphs, and associated collections of conditional indepen...
Penalized estimation in high-dimensional hidden Markov models with state-specific graphical models
HMM Graphical Lasso Universal Regularization Model Selection MMDL Greedy Backwards Pruning Genome Biology Chromatin Modeling
2012/9/17
We consider penalized estimation in hidden Markov models (HMMs) with multi-variate Normal observations. In the moderate-to-large dimensional setting, estimation for HMMs remains challenging in practic...
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...
Sequential detection of multiple change points in networks: a graphical model approach
Sequential detection of multiple change points in networks graphical model approach
2012/9/19
We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for cer-tain functionals of ch...
PC algorithm for Gaussian copula graphical models
Copula covariance matrix graphical model model selection multi-variate normal distribution nonparanormal distribution.
2012/9/18
The PC algorithm uses conditional independence tests for model selection in graphical modeling with acyclic directed graphs. In Gaussian mod-els, tests of conditional independence are typically based ...