scholarly journals On joint estimation of Gaussian graphical models for spatial and temporal data

Biometrics ◽  
2017 ◽  
Vol 73 (3) ◽  
pp. 769-779 ◽  
Author(s):  
Zhixiang Lin ◽  
Tao Wang ◽  
Can Yang ◽  
Hongyu Zhao
Keyword(s):  
Graphical Models ◽  
Joint Estimation ◽  
Temporal Data ◽  
Gaussian Graphical Models

scholarly journals Jewel: A Novel Method for Joint Estimation of Gaussian Graphical Models

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2105
Author(s):  
Claudia Angelini ◽  
Daniela De De Canditiis ◽  
Anna Plaksienko
Keyword(s):  
Graphical Models ◽  
Regulatory Networks ◽  
Regularization Parameter ◽  
Estimation Method ◽  
Real Data ◽  
Penalized Regression ◽  
Joint Estimation ◽  
Gaussian Graphical Models ◽  
Lasso Penalty ◽  
Independence Structure

In this paper, we consider the problem of estimating multiple Gaussian Graphical Models from high-dimensional datasets. We assume that these datasets are sampled from different distributions with the same conditional independence structure, but not the same precision matrix. We propose jewel, a joint data estimation method that uses a node-wise penalized regression approach. In particular, jewel uses a group Lasso penalty to simultaneously guarantee the resulting adjacency matrix’s symmetry and the graphs’ joint learning. We solve the minimization problem using the group descend algorithm and propose two procedures for estimating the regularization parameter. Furthermore, we establish the estimator’s consistency property. Finally, we illustrate our estimator’s performance through simulated and real data examples on gene regulatory networks.

scholarly journals High-dimensional joint estimation of multiple directed Gaussian graphical models

2020 ◽  
Vol 14 (1) ◽  
pp. 2439-2483
Author(s):  
Yuhao Wang ◽  
Santiago Segarra ◽  
Caroline Uhler
Keyword(s):  
Graphical Models ◽  
Joint Estimation ◽  
High Dimensional ◽  
Gaussian Graphical Models

Scalable Estimator for Multi-task Gaussian Graphical Models Based in an IoT Network

2021 ◽  
Vol 17 (3) ◽  
pp. 1-33
Author(s):  
Beilun Wang ◽  
Jiaqi Zhang ◽  
Yan Zhang ◽  
Meng Wang ◽  
Sen Wang
Keyword(s):  
Graphical Models ◽  
Large Scale ◽  
Graphical Model ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Joint Estimation ◽  
Gaussian Graphical Models ◽  
Novel Approach ◽  
Cloud Server ◽  
Iot Devices

Recently, the Internet of Things (IoT) receives significant interest due to its rapid development. But IoT applications still face two challenges: heterogeneity and large scale of IoT data. Therefore, how to efficiently integrate and process these complicated data becomes an essential problem. In this article, we focus on the problem that analyzing variable dependencies of data collected from different edge devices in the IoT network. Because data from different devices are heterogeneous and the variable dependencies can be characterized into a graphical model, we can focus on the problem that jointly estimating multiple, high-dimensional, and sparse Gaussian Graphical Models for many related tasks (edge devices). This is an important goal in many fields. Many IoT networks have collected massive multi-task data and require the analysis of heterogeneous data in many scenarios. Past works on the joint estimation are non-distributed and involve computationally expensive and complex non-smooth optimizations. To address these problems, we propose a novel approach: Multi-FST. Multi-FST can be efficiently implemented on a cloud-server-based IoT network. The cloud server has a low computational load and IoT devices use asynchronous communication with the server, leading to efficiency. Multi-FST shows significant improvement, over baselines, when tested on various datasets.

scholarly journals Fast hybrid Bayesian integrative learning of multiple gene regulatory networks for type 1 diabetes

Biostatistics ◽  
2019 ◽  
Author(s):  
Bochao Jia ◽  
Faming Liang ◽  
Keyword(s):  
Type 1 Diabetes ◽  
Graphical Models ◽  
Regulatory Networks ◽  
Joint Estimation ◽  
Estimation Accuracy ◽  
Multiple Time ◽  
Integrative Learning ◽  
Gaussian Graphical Models ◽  
Multiple Time Points

SUMMARYMotivated by the study of the molecular mechanism underlying type 1 diabetes with gene expression data collected from both patients and healthy controls at multiple time points, we propose a hybrid Bayesian method for jointly estimating multiple dependent Gaussian graphical models with data observed under distinct conditions, which avoids inversion of high-dimensional covariance matrices and thus can be executed very fast. We prove the consistency of the proposed method under mild conditions. The numerical results indicate the superiority of the proposed method over existing ones in both estimation accuracy and computational efficiency. Extension of the proposed method to joint estimation of multiple mixed graphical models is straightforward.

scholarly journals Joint estimation of multiple dependent Gaussian graphical models with applications to mouse genomics

Biometrika ◽  
2016 ◽  
Vol 103 (3) ◽  
pp. 493-511 ◽  
Author(s):  
Yuying Xie ◽  
Yufeng Liu ◽  
William Valdar
Keyword(s):  
Graphical Models ◽  
Joint Estimation ◽  
Gaussian Graphical Models

scholarly journals Fast Bayesian inference in large Gaussian graphical models

Biometrics ◽  
2019 ◽  
Vol 75 (4) ◽  
pp. 1288-1298
Author(s):  
Gwenaël G. R. Leday ◽  
Sylvia Richardson
Keyword(s):  
Bayesian Inference ◽  
Graphical Models ◽  
Gaussian Graphical Models
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