0538247 - ÚTIA 2021 CZ eng V - Výzkumná zpráva
Barber, Alec - Quinn, Anthony
Bayesian transfer learning between autoregressive inference tasks.
Praha: ÚTIA AV ČR, 2020. Research Report, 2389.
Grant CEP: GA ČR(CZ) GA18-15970S
Institucionální podpora: RVO:67985556
Klíčová slova: autoregression * transfer learning * Fully Probabilistic Design * FPD * food-commodities price prediction
Obor OECD: Applied mathematics
Web výsledku:
http://library.utia.cas.cz/separaty/2021/AS/quinn-0538247.pdf

Bayesian transfer learning typically relies on a complete stochastic dependence speci cation between source and target learners which allows the opportunity for Bayesian conditioning. We advocate that any requirement for the design or assumption of a full model between target and sources is a restrictive form of transfer learning.
Trvalý link: http://hdl.handle.net/11104/0316079