Description Usage Arguments Details Value See Also Examples
View source: R/score-multdir.R
The data must be discrete. The conditional distributions of each random variable, conditional on its parents are assumed to be multinomial, with Dirichlet priors for the parameters.
1 2 3 4 | ## S3 method for class 'bn'
logScoreMultDir(x, data,
cache = new.env(hash = T), hyperparameters = "bdeu",
checkInput = T, ...)
|
x |
An object of class "bn". The Bayesian Network by for which the marginal likelihood is computed. |
data |
A data.frame, with columns being factors giving the values of each random variable. |
cache |
Optionally, provide an environment with cached local scores for this data. |
hyperparameters |
A character vector of length one. Either "bdeu", "qi", "one", or "point9" |
checkInput |
A logical of length 1, specifying whether to check the inputs to the function. |
... |
Further arguments, currently unused |
The notation here roughly follows Mukherjee and Speed (2008) Network inference using informative priors. PNAS 105 (38) 14313-14318, doi: 10.1073/pnas.0802272105
A numeric vector of length 1, giving the log marginal likelihood. The environment 'cache' will also be updated because its scope is global.
logScoreMultDir
,
logScoreMultDir.bn.list
1 2 3 4 5 |
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