SummaryMarginals: Summary a continuous marginal distribution

In BayesNetBP: Bayesian Network Belief Propagation

Summary a continuous marginal distribution

Description

This function summary the marginal distributions of continuous variables by outputing the mean, standard deviation, and number of subpopulations

Usage

SummaryMarginals(marginals)

Arguments

marginals

the marginal distributions obtained from Marginals function

Value

a data.frame object containing information about the marginal distributions for continuous variables. The marginal distributions of continous variables in a CG-BN model are mixtures of Gaussian distributions. Therefore, besides the mean and standard deviation, the object has an additional column to specify the number of Gaussian mixtures.

mean

the mean value of a Gaussian distribution.

sd

the standard deviation of a Gaussian distribution.

n

the number of Gaussian distributions in the mixture.

References

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

Yu H, Moharil J, Blair RH (2020). BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks. Journal of Statistical Software, 94(3), 1-31. <doi:10.18637/jss.v094.i03>.

See Also

Marginals

Examples

data(liver)
tree.init.p <- Initializer(dag=liver$dag, data=liver$data,
                           node.class=liver$node.class,
                           propagate = TRUE)
marg <- Marginals(tree.init.p, c("HDL", "Ppap2a", "Neu1"))
SummaryMarginals(marginals=marg)

BayesNetBP documentation built on May 9, 2022, 1:05 a.m.