A penalized complexity prior for deep Bayesian transfer learning with application to materials informatics

DOI10.1214/23-aoas1759arXiv2201.12401OpenAlexW4388055219MaRDI QIDQ6138640

Mohamed A. Abba, Brian J. Reich, Jonathan P. Williams

Publication date: 16 January 2024

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2201.12401

zbMATH Keywords

neural networksKullback-Leibler divergencematerials sciencevariational Bayesian inference

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

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• BART: Bayesian additive regression trees
• Penalising model component complexity: a principled, practical approach to constructing priors
• Optimal Bayesian Transfer Learning

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