This thesis deals with the introduction of novel Bayesian methods for the analysis of high-dimensional data. The methods assess the following problems: - variable selection when the enumartion of the space of competing models is prevented; - Bayesian network analysis for the estimation of multiple correlated precision matrices; - improving prediction and variable selection in a regression problem by exploiting prior external information on the covariates.

Bayesian methods for high-dimensional applications / Claudio Busatto. - (2023).

Bayesian methods for high-dimensional applications

Claudio Busatto
2023

Abstract

This thesis deals with the introduction of novel Bayesian methods for the analysis of high-dimensional data. The methods assess the following problems: - variable selection when the enumartion of the space of competing models is prevented; - Bayesian network analysis for the estimation of multiple correlated precision matrices; - improving prediction and variable selection in a regression problem by exploiting prior external information on the covariates.
2023
Claudio Francesco Stingo
ITALIA
Claudio Busatto
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1319157
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