Introduction to data visualization and characterization
Random vectors and the multivariate normal distribution
Explaining covariance structure : principal components
Linear model for numerical and categorical response variables
Linear mixed effects model
Diagnosis of variation source using PCA
Diagnosis of variation sources through random effects estimation
Analysis of system diagnosability
Prognosis through mixed effects models for longitudinal data
Prognosis using Gaussian process model
Prognosis through mixed effects models for time-to-event data.