LocalModelCompile: Model compilation

In Yam76/BayesNetBP: Bayesian Network Belief Propagation

Description

Compile the local models

Usage

LocalModelCompile(data, dag = NULL, node.class = NULL)

Arguments

data	a data.frame object or a qtlnet object
dag	NULL if data is qtlnet object, or a graphNEL object of conditional Gaussian Bayesian network if data is data.frame.
node.class	NULL if data is qtlnet object, or a vector of logical values named by node names, TRUE for discrete, FALSE for continuous variables if data is data.frame.

Details

This function compiles the local models, including the conditional probability tables for discrete variables, and linear predictor potentials for continuous variables.

Value

pots

a list of discrete potentials (conditional probability tables) for each discrete variable.

bags

a list of sets of continuous potentials (lppotentials), each set for a continuous variables.

Author(s)

Han Yu

References

Cowell, R. G. (2005). Local propagation in conditional Gaussian Bayesian networks. Journal of Machine Learning Research, 6(Sep), 1517-1550.

See Also

ElimTreeInitialize

Examples

data(liver)
models <- LocalModelCompile(data=liver$data, dag=liver$dag, node.class=liver$node.class)

Yam76/BayesNetBP documentation built on Aug. 23, 2019, 1:23 a.m.