Posterior Predictive Samples
Provides samples from the posterior predictive distribution.
posterior.predict( object, iter = 1, ... )
object: object of S3 class "bdgraph", from function bdgraph.iter: number of predictions....: additional parameters.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, 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, 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, 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, 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, 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, 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, tools:::Rd_expr_doi("10.1111/rssc.12171")
a matrix containing the predicted datasets, corresponding to the samples from the joint posterior disribtuion.
Reza Mohammadi a.mohammadi@uva.nl and Ernst Wit
bdgraph, bdgraph.mpl, bdgraph.dw
## 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)