Bayes factor between two network structures
Compute the Bayes factor between the structures of two Bayesian networks..
BF(num, den, data, score, ..., log = TRUE)
num, den |
two objects of class |
data |
a data frame containing the data to be used to compute the Bayes factor. |
score |
a character string, the label of a posterior network score.
If none is specified, the default score is the Bayesian Dirichlet
equivalent score ( |
... |
extra tuning arguments for the posterior scores. See
|
log |
a boolean value. If |
A single numeric value, the Bayes factor of the two network structures
num and den.
The Bayes factor for two network structures, by definition, is the ratio of
the respective marginal likelihoods which is equivalent to the ration of
the corresponding posterior probabilities if we assume the uniform
prior over all possible DAGs. However, note that it is possible to specify
different priors using the “...” arguments of BF(); in
that case the value returned by the function will not be the classic Bayes
factor.
Marco Scutari
data(learning.test)
dag1 = model2network("[A][B][F][C|B][E|B][D|A:B:C]")
dag2 = model2network("[A][C][B|A][D|A][E|D][F|A:C:E]")
BF(dag1, dag2, learning.test, score = "bds", iss = 1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.