Experiments in stochastic computation for high-dimensional graphical models

DOI10.1214/088342305000000304zbMath1130.62408OpenAlexW2012328630MaRDI QIDQ2381758

Mike West, Chris Carter, Beatrix Jones, Chris Hans, Adrian Dobra, Carlos Marinho Carvalho

Publication date: 18 September 2007

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ss/1137076659

zbMATH Keywords

Markov chain Monte Carloparallel implementationdecomposable modelsshotgun stochastic searchnondecomposable models

Mathematics Subject Classification ID

Applications of statistics (62P99) Numerical analysis or methods applied to Markov chains (65C40)

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Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Positive definite completions of partial Hermitian matrices
• Hyper Markov laws in the statistical analysis of decomposable graphical models
• Improving Markov chain Monte Carlo model search for data mining
• Graphical models for genetic analyses
• Sparse graphical models for exploring gene expression data
• 10.1162/153244301753344614
• Efficient estimation of covariance selection models
• Analogies between Multiplicative Models in Contingency Tables and Covariance Selection
• Probabilistic Networks and Expert Systems
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
• Bounds for cell entries in contingency tables given marginal totals and decomposable graphs
• Bayesian Graphical Models for Discrete Data
• Markov chain Monte Carlo model determination for hierarchical and graphical log-linear models
• Decomposable graphical Gaussian model determination
• The Weighted Likelihood Ratio, Linear Hypotheses on Normal Location Parameters
• A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models