Seminar

I jotted down only a few keywords that might be reusable. I didn’t understand any of the talks.

Functional Data Analysis

• Goal: predict equipment temperature
• Tools: Fourier coefficients (trigo ones), followed by discretisation, min-error estimation, cross-validation 10-folds, $R^2$ adjusted ?, MAE, MSPE
• Comparison with non-functional data

Tolérancement

• Thème : Traiter les incertitudes sur les dimensions des pièces de l’avion
• Objectif :
  • établir une modélisation mathématiques
  • construire un virtual twin de l’avion
• Outils :
  • Modèle de variabilité
  • Modèle d’assemblage $\text{airbus}: Y = \sum_{i = 1}^n a_iX_i?$
  • Notion de risque … calculs des coefficients de convolution

SVM

• Multiclass vs structual, hidden Markov model
• Plan for this year:
  • apply structual SVM for real SVM
  • apply structual SVM for deep neural network

Auxiliary information

• auxiliary function given in one partition
• auxiliary function given in mutiple partitions
• bootstrap
• law of iterated logarithms
• Kullback–Leibler distance
• convergence: Donsker class, var, covar
• ranking ration method: convergence to Gaussian process, entropy conditions, Telegrandś inequality
  • weak convergence: KMT, Berthet-Maison
  • strong convergence: ?
    • consequences: Berry-Essen bound, bias & variance estimation of ranking ration method

Euler scheme SDE

I could only write “Toeplitz tape operator”.