Functional graphical model for spectrometric data analysis

Raffaele Argiento, Lucia Paci, Alessia Pini, Laura Codazzi, Alessandro Colombi, Matteo Gianella

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

Motivated by the analysis of spectrographic data, we introduce a functional graphical model for learning the conditional independence structure of spectra. Absorbance spectra are modeled as continuous functional data through a cubic B-spline basis expansion. A Gaussian graphical model is assumed for basis ex- pansion coefficients, where a sparse structure is induced for the precision matrix. Bayesian inference is carried out, providing an estimate of the precision matrix of the coefficients, which translates into an estimate of the conditional independence structure between frequency bands of the spectrum. The proposed model is applied to the analysis of the infrared absorbance spectra of strawberry purees.
Lingua originaleEnglish
Titolo della pubblicazione ospiteBook of short papers SIS 2020
Pagine852-856
Numero di pagine5
Stato di pubblicazionePubblicato - 2020
Evento50th Meeting of the Italian Statistical Society - Pisa
Durata: 22 giu 202024 giu 2020

Convegno

Convegno50th Meeting of the Italian Statistical Society
CittàPisa
Periodo22/6/2024/6/20

Keywords

  • Bayesian inference, functional data analysis, graphical model selection

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