graphicalEvidence-package {graphicalEvidence} | R Documentation |
Compute Marginal Likelihood in Gaussian Graphical Models
Description
This package allows estimation of marginal likelihood in Gaussian graphical
models through a novel telescoping block decomposition of the precision
matrix which allows estimation of model evidence via an application of
Chib's method. The currently implemented priors are: Bayesian graphical lasso
(BGL), Graphical horseshoe (GHS), Wishart, and G-Wishart. The top level
function used to estimate marginal likelihood is evidence
which
expects the prior name, data, and relevant prior specific parameters. This
package also provides an MCMC prior sampler for the priors of BGL, GHS, and
G-Wishart, implemented in prior_sampling
, which expects a prior
name and prior specific parameters. Both functions also expect the number of
burn-in iterations and the number of sampling iterations for the underlying
MCMC sampler.
Bhadra, A., Sagar, K., Rowe, D., Banerjee, S., & Datta, J. (2022) "Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix." <https://arxiv.org/abs/2205.01016>
Chib, S. "Marginal likelihood from the Gibbs output." (1995) <https://www.jstor.org/stable/2291521>
Details
This package implements marginal estimation for four priors, "Wishart"", Bayesian Graphical Lasso ("BGL"), graphical horseshoe ("GHS"), and "G-Wishart". An MCMC prior sampler is also provided for "BGL", "GHS", and "G-Wishart".
For more information and a faster, less portable implementation, visit the package repository on GitHub: https://github.com/dp-rho/graphicalEvidence
Author(s)
Maintainer: David Rowe <david@rowe-stats.com>
References
Bhadra, A., Sagar, K., Rowe, D., Banerjee, S., & Datta, J. (2022) "Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix." <https://arxiv.org/abs/2205.01016>
Chib, S. "Marginal likelihood from the Gibbs output." (1995) <https://www.jstor.org/stable/2291521>
See Also
test_evidence
: For basic example of functionality
evidence
: For top level estimation function
prior_sampling
: For the prior sampler function
Examples
test_results <- test_evidence(num_runs=3, prior_name='G_Wishart')