localLogScoreMultDir: Local Multinomial-Dirichlet Log marginal likelihood.

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

Description

Compute the LOCAL log marginal likelihood of the supplied Bayesian Networks. ie the contribution to the log marginal liklihood from one individual node.

Usage

  localLogScoreMultDir(node, parents, logScoreParameters,
    cache, checkInput = T)

Arguments

node	A numeric vector of length 1. The node to compute the local log score for.
parents	A numeric vector. The parents of node.
logScoreParameters	A list with the following components: data A matrix (NOT data.frame), with columns being integers in the range 0, 1, 2, .... giving the values of each random variable. **** The integers MUST start numbering at 0 NOT 1 **** nl A numeric vector of length ncol(data), specifying the number of levels that each random variable takes. hyperparameters A character vector of length one. Either "bdeu", "qi", "one", or "point9"
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

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, logScoreMultDirIncremental, logScoreMultDirOffline

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