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Is there a future for stochastic modeling in business and industry in the era of machine learning and artificial intelligence?

  • Fabrizio RUGGERI
  • , David BANKS
  • , William S. CLEVELAND
  • , Nicholas I. FISHER
  • , Marcos ESCOBAR-ANEL
  • , Paolo GIUDICI
  • , Emanuela RAFFINETTI
  • , Roger W. HOERL
  • , Dennis K. J. LIN
  • , Ron S. KENETT
  • , Wai Keung LI
  • , Leung Ho Philip YU
  • , Jean Michel POGGI
  • , Marco S. REIS
  • , Gilbert SAPORTA
  • , Piercesare SECCHI
  • , Rituparna SEN
  • , Ansgar STELAND
  • , Zhanpan ZHANG

Research output: Contribution to journalArticlespeer-review

Abstract

The paper arises from the experience of Applied Stochastic Models in Business and Industry which has seen, over the years, more and more contributions related to Machine Learning rather than to what was intended as a stochastic model. The very notion of a stochastic model (e.g., a Gaussian process or a Dynamic Linear Model) can be subject to change: What is a Deep Neural Network if not a stochastic model? The paper presents the views, supported by examples, of distinguished researchers in the field of business and industrial statistics. They are discussing not only whether there is a future for traditional stochastic models in the era of Machine Learning and Artificial Intelligence, but also how these fields can interact and gain new life for their development. Copyright © 2025 John Wiley & Sons Ltd.

Original languageEnglish
Article numbere70004
JournalApplied Stochastic Models in Business and Industry
Volume41
Early online dateMar 2025
DOIs
Publication statusPublished - 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

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