A hierarchical Bayesian regression model that reduces uncertainty in material demand predictions-Reference-Cited by-同舟云学术

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A hierarchical Bayesian regression model that reduces uncertainty in material demand predictions

Published:2022-10-17 Issue:1 Volume:27 Page:43-55
ISSN:1088-1980
Container-title:Journal of Industrial Ecology
language:en
Short-container-title:J of Industrial Ecology

Author:

Bhuwalka Karan¹^ORCID,Choi Eunseo²,Moore Elizabeth A.¹^ORCID,Roth Richard¹,Kirchain Randolph E.¹^ORCID,Olivetti Elsa A.³^ORCID

Affiliation:

1. Materials Research Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA

2. Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge Massachusetts USA

3. Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA

Publisher

Wiley

Subject

General Social Sciences,General Environmental Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jiec.13339

Reference50 articles.

1. The Life Cycle of Copper, Its Co-Products and Byproducts

2. Scenario types and techniques: Towards a user's guide

3. Exploring future copper demand, recycling and associated greenhouse gas emissions in the EU-28

4. Explaining variation in steel consumption in the OECD

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1. MAGRU: Multi-layer Attention with GRU for Logistics Warehousing Demand Prediction;KSII Transactions on Internet and Information Systems;2024-03-31

2. A Novel Method of Material Requirement Forecasting for Discrete Manufacturing System Based on Improved Genetic Algorithm;Lecture Notes in Mechanical Engineering;2024

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