Bayesian transfer learning between autoregressive inference tasks

0538247 20240103225228.120210121235959.9 Bayesian transfer learning between autoregressive inference tasks Praha ÚTIA AV ČR 2020 P Research Report 2389 autoregression transfer learning Fully Probabilistic Design FPD food-commodities price prediction cav_un_auth*0403479 Barber Alec UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS IE S Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0370768 Quinn Anthony UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems IE Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2021/AS/quinn-0538247.pdf GA18-15970S GA ČR CZ cav_un_auth*0362986 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. BD 10000 10100 10102 2021 2 4 O 4o 20231122145512.8 RVO:67985556 http://hdl.handle.net/11104/0316079 S 2020 Soubory v repozitáři: 0538247.pdf 2020 Praha ÚTIA AV ČR