Bayesian learning of multiple directed networks from observational data
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Publication:6617424
DOI10.1002/sim.8751zbMATH Open1546.62135MaRDI QIDQ6617424
Federico Castelletti, Stefano Peluso, Luca La Rocca, Francesco C. Stingo, Guido Consonni
Publication date: 10 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
Cites Work
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- Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
- Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
- Modeling dependent gene expression
- Bayesian graphical models for differential pathways
- A Bayesian graphical modeling approach to microRNA regulatory network inference
- Discrete chain graph models
- Robust Bayesian graphical modeling using Dirichlet \(t\)-distributions
- Estimating the dimension of a model
- A characterization of Markov equivalence classes for acyclic digraphs
- Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks
- Sparse graphical models for exploring gene expression data
- Optimal predictive model selection.
- Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways
- Exact estimation of multiple directed acyclic graphs
- Alternative Markov properties for chain graphs
- Direct estimation of differential networks
- Node-Based Learning of Multiple Gaussian Graphical Models
- 10.1162/153244302760200696
- The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
- Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics
- Objective Bayes Covariate‐Adjusted Sparse Graphical Model Selection
- Bayesian Inference of Multiple Gaussian Graphical Models
- Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
- Identifiability of Gaussian structural equation models with equal error variances
- A Unified Approach to the Characterization of Equivalence Classes of DAGs, Chain Graphs with no Flags and Chain Graphs
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