Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering

By Chris Fraley, Adrian E. Raftery. Journal of Classification, 2007

456 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/581da8a419488a2c2e5331fa4694b097c1ba98e2

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Citations in the corpus

MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering † (2007, methodology): “A comprehensive treatment of the methods used in MCLUST can be found in [11, 13].”
Defining pyromes and global syndromes of fire regimes (2013, methodology): “Importantly, it also allows for estimation of uncertainty around classifications by using expectation maximization methods (52), and for predicting the grouping of points not used in the original analysis based on their attributes.”
Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models (2015, methodology): “Family Technique Abbreviation Statistical Techniques Naive Bayes NB Simple Logistic SL Clustering Techniques K-means K-means Expectation Maximization EM Rule-Based Techniques Repeated Incremental Pruning to Produce Error Reduction Ripper Ripple Down Rules Ridor Neural Networks Radial Basis Functi”
A Bayesian information criterion for singular models (2013, background): “der and Wasserman (1997) used posterior means. Similarly, one could consider posterior modes/penalized likelihood methods to stay closer to a fully Bayesian analysis or simply for regularization; see Fraley and Raftery (2007) and Baudry and Celeux (2015) for work on Gaussian mixtures. We note that p”
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models (2016, methodology): “It has been used in a broad range of contexts including geochemistry (Templ et al., 2008; Ellefsen et al., 2014), chemometrics (Fraley and Raftery, 2006a, 2007b), DNA sequence analysis (Verbist et al., 2015), gene expression data (Yeung et al., 2001; Li et al., 2005; Fraley and Raftery, 2006b),…”
Model-based Methods of Classification: Using the mclust Software in Chemometrics (2007, methodology): “The current version of mclust also includes a Bayesian prior for regularization for datasets in which maximum likelihood estimation fails due to singularities or degeneracies (Fraley and Raftery 2005).”
Penalized Model-Based Clustering with Application to Variable Selection (2007, background): “Recently Fraley and Raftery (2005) proposed a Bayesian approach to regularizing model-based clustering; there is a large body of literature on Bayesian mixture modeling, e.g., Richardson and Green (1997), Jasra et al. (2005) and references therein.”
mclust Version 4 for R : Normal Mixture Modeling for Model-Based Clustering , Classification , and Density Estimation (2012, methodology): “A comprehensive treatment of the methods used in mclust can be found in [13, 17, 31, 3].”