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Conditional Mixture Model and Its Application for Regression Model
[post]

2020
unpublished

Expectation maximization (EM) algorithm is a powerful mathematical tool for estimating statistical parameter when data sample contains hidden part and observed part. EM is applied to learn finite mixture model in which the whole distribution of observed variable is average sum of partial distributions. Coverage ratio of every partial distribution is specified by the probability of hidden variable. An application of mixture model is soft clustering in which cluster is modeled by hidden variable

doi:10.20944/preprints202010.0550.v2
fatcat:c3gihvnivjgpxee7pw3u3elhnu