EconPapers    
Economics at your fingertips  
 

Retraining as Approximate Bayesian Inference

Harrison Katz

Foresight: The International Journal of Applied Forecasting, 2026, issue 81, 43-48

Abstract: Model retraining is usually treated as an ongoing maintenance task. But as the author argues, retraining can be better understood as approximate Bayesian inference under computational constraints. Copyright International Institute of Forecasters, 2026

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://forecasters.org/foresight/bookstore/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:for:ijafaa:y:2026:i:81:p:43-48

Access Statistics for this article

More articles in Foresight: The International Journal of Applied Forecasting from International Institute of Forecasters Contact information at EDIRC.
Bibliographic data for series maintained by Michael Gilliland ().

 
Page updated 2026-04-03
Handle: RePEc:for:ijafaa:y:2026:i:81:p:43-48