Mixture Models for Model Based Clustering and Classification

Dottorato in Matematica Informatica e Statistica (UniFI, UniPG, INdAM)
Dottorato in Economia (UniPG)

Contents


  • Finite mixture models
  • Gaussian Mixture Models
  • Model‐based clustering
  • The R package mclust
  • EM algorithm
  • Model selection
  • Density estimation via finite mixture modeling
  • Classification using Gaussian mixture models

Reading list


  • Printed slides (provided in class to participants)
  • Fraley C., Raftery A. E. (2002) Model‐based clustering, discriminant analysis, and density estimation. JASA, 97(458), 611-631.
  • Fraley C., Raftery A. E., Murphy T. B., Scrucca L. (2012) MCLUST Version 4 for R: Normal Mixture Modeling for Model‐Based Clustering, Classification, and Density Estimation. Tech. Rep. 597, Department of Statistics, University of Washington.
  • McLachlan G., Peel D. (2000) Finite Mixture Models. New York: Wiley.
  • Scrucca L., Fop. M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R Journal, 8/1, 205-233.