搜索结果: 1-15 共查到“理论统计学 Graphical”相关记录20条 . 查询时间(0.068 秒)
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...
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...
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...
Marginal log-linear parameters for graphical Markov models
multivariate discrete statistical models parametrization marginal log-linear graphical Markov models
2011/6/20
The parametrization of multivariate discrete statistical models by marginal log-linear
(MLL) parameters provides a great deal of flexibility; in particular, different MLL parametrizations
under line...
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...
Standard imsets for undirected and chain graphical models
conditional independence decomposable graph max-imal prime subgraph triangulation
2011/3/21
We derive standard imsets for undirected graphical models and chain graphical models. Standard imsets for undirected graphical models are described in terms of minimal triangulations for maximal prime...
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...
Learning the Structure of Deep Sparse Graphical Models
Structure Deep Sparse Graphical Models deep belief networks
2010/3/9
Deep belief networks are a powerful way to model complex probability
distributions. However, learning the structure of a belief network,
particularly one with hidden units, is difficult. The Indian...
Dynamic Matrix-Variate Graphical Models
Bayesian Forecasting Dynamic Linear Models Gaussian Graphical Models Graphical Model Uncertainty Hyper-Inverse Wishart Distribution
2009/9/22
This paper introduces a novel class of Bayesian models for multivariate
time series analysis based on a synthesis of dynamic linear models and graphical
models. The synthesis uses sparse graphical m...
Variational Bayesian Learning of Directed Graphical Models with Hidden Variables
Approximate Bayesian Inference Bayes Factors Directed Acyclic Graphs EM Algorithm Graphical Models Markov Chain Monte Carlo
2009/9/21
A key problem in statistics and machine learning is inferring suitable
structure of a model given some observed data. A Bayesian approach to model
comparison makes use of the marginal likelihood of ...
GRAPHICAL REPRESENTATION OF SOME DUALITY RELATIONS IN STOCHASTIC POPULATION MODELS
DUALITY RELATIONS STOCHASTIC POPULATION MODELS REPRESENTATION
2009/3/23
We derive a unified stochastic picture for the duality of a resampling-selection model with a branching-coalescing particle process (cf. MR2123250) and for the self-duality of Feller's branching diffu...
Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling
Adaptive Lasso High Dimensional Regression Gaussian Graphical Modeling
2010/3/18
We show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent for high-dimensional
model selection in linear and Gaussian graphical models. Our conditions for consistency cover more
...
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...