logScoreNormal.bn: Normal-inverse-gamma (with g-prior) Log marginal likelihood.

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

Description

Compute the log marginal likelihood of the supplied Bayesian Network.

Usage

  ## S3 method for class 'bn'
 logScoreNormal(x, data,
    cache = new.env(hash = T), checkInput = T, ...)

Arguments

x	An object of class "bn". The Bayesian Network by for which the marginal likelihood is computed.
data	A matrix with columns giving the value of each random variable.
cache	Optionally, provide an environment with cached local scores for this data.
checkInput	A logical of length 1, specifying whether to check the inputs to the function.
...	Further arguments, currently unused

Details

The data is scored as continuous, using a form of the Normal Prior.

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

logScoreNormal, logScoreNormal.bn.list

Examples

data <- cbind(c(-10, -2), c(-11, -4))
net <- bn(integer(0), 1)
logScoreNormal(net, data)

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