Description Usage Arguments Author(s) References See Also Examples

Prints the information about the selected graph which could be a graph with links for which their estimated posterior probabilities are greater than 0.5 or graph with the highest posterior probability. It provides adjacency matrix, size and posterior probability of the selected graph.

1 2 |

`x` |
An object of |

`round` |
A value to round the probabilities to the specified number of decimal places (default is 3). |

`...` |
System reserved (no specific usage). |

Abdolreza Mohammadi and Ernst Wit

Mohammadi, A. and E. Wit (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, *Bayesian Analysis*, 10(1):109-138

Mohammadi, A. and E. Wit (2015). BDgraph: An `R`

Package for Bayesian Structure Learning in Graphical Models, *arXiv:1501.05108*

Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, *Journal of the Royal Statistical Society: Series C*

Mohammadi, A., Massam H., and G. Letac (2017). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, *arXiv:1706.04416*

1 2 3 4 5 6 7 8 9 | ```
## Not run:
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, vis = TRUE )
bdgraph.obj <- bdgraph( data = data.sim )
print( bdgraph.obj )
## End(Not run)
``` |

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