Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
From MaRDI portal
Publication:3225807
DOI10.1198/jasa.2011.tm10465zbMath1234.62018arXiv1005.4094OpenAlexW2056720303WikidataQ31049961 ScholiaQ31049961MaRDI QIDQ3225807
Abel Rodríguez, Adrian Dobra, Alex Lenkoski
Publication date: 22 March 2012
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1005.4094
Multivariate distribution of statistics (62H10) Directional data; spatial statistics (62H11) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)
Related Items
Graph-based multivariate conditional autoregressive models, Bayesian structure learning in sparse Gaussian graphical models, Scaling it up: stochastic search structure learning in graphical models, Restricted covariance priors with applications in spatial statistics, Bayesian model selection approach for coloured graphical Gaussian models, Reciprocal graphical models for integrative gene regulatory network analysis, Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo, MCMC Algorithms for Posteriors on Matrix Spaces, The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models, Bayesian graphical models for modern biological applications, Unnamed Item, Scalable Bayesian matrix normal graphical models for brain functional networks, Incorporating grouping information into Bayesian Gaussian graphical model selection, On Joint Estimation of Gaussian Graphical Models for Spatial and Temporal Data, A loss‐based prior for Gaussian graphical models, Unnamed Item, The performance of covariance selection methods that consider decomposable models only, Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions, Hierarchical Gaussian graphical models: beyond reversible jump, Simulation of hyper-inverse Wishart distributions for non-decomposable graphs, Sparse covariance estimation in heterogeneous samples, Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Spatially varying SAR models and Bayesian inference for high-resolution lattice data, Posterior convergence rates for estimating large precision matrices using graphical models, Separable factor analysis with applications to mortality data, Bayesian Double Feature Allocation for Phenotyping With Electronic Health Records, Exact formulas for the normalizing constants of Wishart distributions for graphical models, Bayesian graphical models for differential pathways, Learning Gaussian graphical models with fractional marginal pseudo-likelihood, Loglinear model selection and human mobility, Quasi-Bayesian estimation of large Gaussian graphical models, A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data, Bayesian Approaches for Large Biological Networks, Efficient local updates for undirected graphical models, Hierarchical normalized completely random measures for robust graphical modeling, Different types of Bernstein operators in inference of Gaussian graphical model, An Expectation Conditional Maximization Approach for Gaussian Graphical Models, Bayesian Regularization for Graphical Models With Unequal Shrinkage, Estimating heterogeneous gene regulatory networks from zero-inflated single-cell expression data, Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks, Unnamed Item, An Integrative Bayesian Modeling Approach to Imaging Genetics
