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Found 4 papers, 2 papers with code
Large-scale Bayesian Structure Learning for Gaussian Graphical Models using Marginal Pseudo-likelihood
Our simulation study indicates that the proposed algorithms, particularly for large-scale sparse graphs, outperform the leading Bayesian approaches in terms of computational efficiency and precision.
Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models
In a Bayesian framework for regression trees, Markov Chain Monte Carlo (MCMC) search algorithms are required to generate samples of tree models according to their posterior probabilities.
Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models
In this class of models, Bayesian structure learning is often done by search algorithms over the graph space.
BDgraph: An R Package for Bayesian Structure Learning in Graphical Models
Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning.
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