The <i>G</i>-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models-Reference-Cited by-同舟云学术

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The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models

Published:2022-04-04 Issue:4 Volume:31 Page:1215-1224
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

van den Boom Willem¹^ORCID,Beskos Alexandros²³^ORCID,De Iorio Maria¹²⁴^ORCID

Affiliation:

1. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore;;

2. Department of Statistical Science, University College London, London, UK;;

3. Alan Turing Institute, London, UK;;

4. Singapore Institute for Clinical Sciences, A*STAR, Singapore, Singapore

Funder

Singapore Ministry of Education Academic Research Fund Tier 2

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2022.2050250

Reference52 articles.

1. A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models

2. Monte Carlo Calculations of the Radial Distribution Functions for a Proton?Electron Plasma

3. An Introduction to Clique Minimal Separator Decomposition

4. Organizing the atoms of the clique separator decomposition into an atom tree

5. Joint High‐Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis

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3. Precision matrix estimation under the horseshoe-like prior–penalty dual;Electronic Journal of Statistics;2024-01-01

4. Learning Block Structured Graphs in Gaussian Graphical Models;Journal of Computational and Graphical Statistics;2023-06-15

5. Bayesian Learning of Graph Substructures;Bayesian Analysis;2022-01-01

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