Description Usage Arguments Value Author(s) References See Also Examples

Compute the Bayes factor between the structure of two graphs.

1 2 |

```
num,
den
``` |
An adjacency matrix corresponding to the true graph structure in which |

`bdgraph.obj` |
An object of |

`log` |
A character value. If TRUE the Bayes factor is given as log(BF). |

A single numeric value, the Bayes factor of the two graph structures `num`

and `den`

.

Reza Mohammadi a.mohammadi@uva.nl

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

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

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

Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, *arXiv preprint arXiv:1706.04416v2*

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, *Annals of Applied Statistics*, 12(2):815-845

`bdgraph`

, `bdgraph.mpl`

, `compare`

, `bdgraph.sim`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## Not run:
# Generating multivariate normal data from a 'circle' graph
data.sim <- bdgraph.sim( n = 50, p = 6, graph = "circle", vis = TRUE )
# Running sampling algorithm
bdgraph.obj <- bdgraph( data = data.sim )
graph_1 <- graph.sim( p = 6, vis = TRUE )
graph_2 <- graph.sim( p = 6, vis = TRUE )
bf( num = graph_1, den = graph_2, bdgraph.obj = bdgraph.obj )
## End(Not run)
``` |

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