Learning from Data via Mixture Models

Salvatore Ingrassia University of Catania (Italy)

  • Date:

    19 JANUARY
    -
    22 JANUARY 2026
     from 14:30 to 17:00
  • Event location: Aula IV, Department of Statistical Sciences, Via Belle Arti 41 (STAT PhD Classes Virtual Room on need)

  • Type: Cycle 41 - Elective Courses

Short course on Learning from Data via Mixture Models

Prof. Salvatore Ingrassia, University of Catania (Italy), salvatore.ingrassia@unict.it

 

The teaching program concerns Mixture Models and their applications. In detail, the following main topics will be presented:

 1. Mixtures of distributions.

a. Gaussian mixtures, Model-Based Clustering, Maximum Likelihood approach to parameter estimation and numerical issues, EM algorithm.

b. Measures of Class Agreement: Confusion matrix, Adjusted Rand Index(ARI).

c. Parsimonious model-based clustering.

 

2. Mixtures of regressions.

a. Mixtures of regression with fixed covariates, mixtures of regression with concomitant variables.

b. Cluster-weighted models, mixtures of generalized linear models.

c. Local and overall R-squared measures for mixtures of regression models.

d. Conditional mixture modeling and model-based clustering.

e. Mixtures of experts.

 

3. Skew Distributions and Related Mixture Models

a. Skew Distributions.

b. Skew-normal and skew-t mixtures.

 

4. Transformation Mixture Models

a. Manly transformation for normality.

b. Bickel-Doksum transformation for normality.

 

Teaching schedule: 10 hrs.