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Learn parameters of a network using the Expectation-Maximization algorithm.
em(x, dataset, threshold = 0.001, max.em.iterations = 10, ess = 1)# S4 method for InferenceEngine,BNDataset em(x, dataset, threshold = 0.001, max.em.iterations = 10, ess = 1)
# S4 method for InferenceEngine,BNDataset em(x, dataset, threshold = 0.001, max.em.iterations = 10, ess = 1)
an InferenceEngine.
InferenceEngine
observed dataset with missing values for the Bayesian Network of x.
x
threshold for convergence, used as stopping criterion.
maximum number of iterations to run in case of no convergence.
Equivalent Sample Size value.
a list containing: an InferenceEngine with a new updated network ("InferenceEngine"), and the imputed dataset ("BNDataset").
"InferenceEngine"
"BNDataset"
# NOT RUN { em(x, dataset) # } # NOT RUN { # }
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