Original title: Bayesian transfer learning between autoregressive inference tasks
Authors: Barber, Alec ; Quinn, Anthony
Document type: Research reports
Year: 2020
Language: eng
Series: Research Report, volume: 2389
Abstract: 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.
Keywords: autoregression; food-commodities price prediction; FPD; Fully Probabilistic Design; transfer learning
Project no.: GA18-15970S (CEP)
Funding provider: GA ČR

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2021/AS/quinn-0538247.pdf
Original record: http://hdl.handle.net/11104/0316079

Permalink: http://www.nusl.cz/ntk/nusl-432902


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