em: expectation-maximization algorithm.

In tavazzie/bnstructScore: Bayesian Network Structure Learning from Data with Missing Values

Description

Learn parameters of a network using the Expectation-Maximization algorithm.

Usage

em(x, dataset, threshold = 0.001, max.em.iterations = 10, ess = 1)

## S4 method for signature 'InferenceEngine,BNDataset'
em(x, dataset, threshold = 0.001, max.em.iterations = 10, ess = 1)

Arguments

x	an InferenceEngine.
dataset	observed dataset with missing values for the Bayesian Network of x.
threshold	threshold for convergence, used as stopping criterion.
max.em.iterations	maximum number of iterations to run in case of no convergence.
ess	Equivalent Sample Size value.

Value

a list containing: an InferenceEngine with a new updated network ("InferenceEngine"), and the imputed dataset ("BNDataset").

Examples

## Not run: 
em(x, dataset)

## End(Not run)

tavazzie/bnstructScore documentation built on Dec. 23, 2021, 7:47 a.m.