CORDIS Project
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This project focuses on developing flexible Bayesian non-parametric models for analyzing complex datasets. By addressing data heterogeneity and clustering, it aims to enhance statistical tools applicable in fields like economics and genetics, particularly in the era of big data.
The use of Bayesian non-parametric (BNP) priors in applied statistical modeling has become increasingly popular in the last few years.
From the seminal paper of Ferguson (1973, Annals of Statistics), the Dirichlet Process and its extensions have been increasingly used to address inferential problems in many fields.
Examples range from variable selection in genetics to linguistics, psychology, human learning , image segmentation and applications to neurosciences.
The increased interest in non-pa…
UNIVERSITY OF KENT
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