Efficient sampling of Gaussian graphical models using conditional Bayes factors
From MaRDI portal
Publication:6537807
DOI10.1002/sta4.66MaRDI QIDQ6537807
M. A. J. Van Gerven, Max Hinne, Alex Lenkoski, Tom Heskes
Publication date: 14 May 2024
Published in: Stat (Search for Journal in Brave)
Cites Work
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Copula Gaussian graphical models and their application to modeling functional disability data
- Conjugate priors for exponential families
- Hyper Markov laws in the statistical analysis of decomposable graphical models
- Bayesian analysis of mixture models with an unknown number of components\,--\,an alternative to reversible jump methods.
- Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions
- Hierarchical Gaussian graphical models: beyond reversible jump
- A Metropolis-Hastings based method for sampling from the \(G\)-Wishart distribution in Gaussian graphical models
- A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants
- Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
- Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
- Reversible Jump, Birth-and-Death and More General Continuous Time Markov Chain Monte Carlo Samplers
- Independence Structure of Natural Conjugate Densities to Exponential Families and the Gibbs' Sampler
- A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
- On Information and Sufficiency
- The Elements of Statistical Learning
- A direct sampler for G-Wishart variates
Related Items (2)
Joint modeling of association networks and longitudinal biomarkers: an application to childhood obesity ⋮ Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison
This page was built for publication: Efficient sampling of Gaussian graphical models using conditional Bayes factors
