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

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

Description

Compute the difference in log marginal likelihood of the supplied Bayesian Networks, quickly.

Usage

  logScoreNormalIncremental(currentBN, proposalBN, heads,
    logScoreParameters, cache, checkInput = T)

Arguments

currentBN	An object of class "bn".
proposalBN	An object of class "bn".
heads	A numeric vector, specifying which nodes have different parents in currentBN and proposalBN.
logScoreParameters	A list with the following components: data A matrix with columns giving the values of each random variable. nl A numeric vector of length nNodes(currentBN), specifying the number of levels that each random variable takes.
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.

Details

This is a fast, incremental version of logScoreNormal.

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

Value

logscore(proposalBN) - logscore(currentBN)

See Also

logScoreNormal, logScoreNormalOffline

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