logScoreMultDir.bn.list: Multinomial-Dirichlet Log marginal likelihood.

In rjbgoudie/structmcmc: Structural inference for Bayesian Networks and Variable Selection.

Description

Compute the log marginal likelihood of the supplied Bayesian Networks.

Usage

  ## S3 method for class 'bn.list'
 logScoreMultDir(x, data,
    hyperparameters = "bdeu", cache = new.env(hash = T),
    verbose = F, ...)

Arguments

x	An object of class "bn.list", the Bayesian Networks for which the marginal likelihood are 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"
verbose	A logical of length 1. If true, a progress bar will be shown.
...	Further arguments (unused)

Details

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.

The notation here roughly follows Mukherjee and Speed (2008) Network inference using informative priors. PNAS 105 (38) 14313-14318, doi: 10.1073/pnas.0802272105

Value

A numeric vector of length 1, giving the log marginal likelihood. The environment 'cache' will also be updated because its scope is global.

See Also

logScoreMultDir, logScoreMultDir.bn, logScoreMultDirOffline, logScoreMultDirIncremental

rjbgoudie/structmcmc documentation built on Nov. 3, 2020, 3:41 a.m.