posterior.predict: Posterior Predictive Samples

In BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Posterior Predictive Samples

Description

Provides samples from the posterior predictive distribution.

Usage

posterior.predict( object, iter = 1, ... )

Arguments

object	object of S3 class "bdgraph", from function bdgraph.
iter	number of predictions.
...	additional parameters.

Value

a matrix containing the predicted datasets, corresponding to the samples from the joint posterior disribtuion.

Author(s)

Reza Mohammadi [e-mail] and Ernst Wit

References

Mohammadi, R. and Wit, E. C. (2019). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, Journal of Statistical Software, 89(3):1-30, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v089.i03")}

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/14-BA889")}

Mohammadi, R., Massam, H. and Letac, G. (2023). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2021.1996377")}

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/18-AOAS1164")}

Vogels, L., Mohammadi, R., Schoonhoven, M., and Birbil, S.I. (2023) Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison, arXiv preprint, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2307.02603")}

Mohammadi, R., Schoonhoven, M., Vogels, L., and Birbil, S.I. (2023) Large-scale Bayesian Structure Learning for Gaussian Graphical Models using Marginal Pseudo-likelihood, arXiv preprint, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2307.00127")}

Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C, 66(3):629-645, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssc.12171")}

See Also

bdgraph, bdgraph.mpl, bdgraph.dw

Examples

## Not run: 
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, size = 7, vis = TRUE )
   
bdgraph.obj <- bdgraph( data = data.sim )
   
posterior.predict( bdgraph.obj, iter = 20 )

## End(Not run)

BDgraph documentation built on Sept. 11, 2024, 5:30 p.m.