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 burnin 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') 

[Package graphicalEvidence version 1.0 Index]