7 citations · 13 across the 6 of their papers we have counts for
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stat.ME2020
Mixture-based estimation of entropy
Stéphane Robin, Luca Scrucca
The entropy is a measure of uncertainty that plays a central role in information theory. When the distribution of the data is unknown, an estimate of the entropy needs be obtained…
stat.ML2020★ 1 cited
Handling missing data in model-based clustering
Alessio Serafini, Thomas Brendan Murphy, Luca Scrucca
Gaussian Mixture models (GMMs) are a powerful tool for clustering, classification and density estimation when clustering structures are embedded in the data. The presence of missin…
stat.ME2020
A fast and efficient Modal EM algorithm for Gaussian mixtures
Luca Scrucca
In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function, where the latter can be estimated either non-parametri…