Work Description
Title: Data and code for paper title "Expert elicitation and data noise learning for material flow analysis using Bayesian inference" Open Access Deposited
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(2025). Data and code for paper title "Expert elicitation and data noise learning for material flow analysis using Bayesian inference" [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/dz6c-5w53
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Files (Count: 6; Size: 375 KB)
Thumbnailthumbnail-column | Title | Original Upload | Last Modified | File Size | Access | Actions |
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Inference.ipynb | 2025-04-04 | 2025-04-04 | 124 KB | Open Access |
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Model-Comparison.ipynb | 2025-04-04 | 2025-04-04 | 51.5 KB | Open Access |
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PDF_Fitting_Dirichlet.ipynb | 2025-04-04 | 2025-04-04 | 56 KB | Open Access |
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Sankey_2012_data.xlsx | 2025-04-04 | 2025-04-04 | 114 KB | Open Access |
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MFA_code_1_ReadMe.txt | 2025-04-18 | 2025-04-18 | 2.72 KB | Open Access |
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Expert_Weighting.xlsx | 2025-04-18 | 2025-04-18 | 26.9 KB | Open Access |
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Date: 4 April, 2025
Dataset Title: Data and code for paper title "Expert elicitation and data noise learning for material flow analysis using Bayesian inference"
Dataset Contact: [email protected]
Dataset Creators:
Name: Jiayuan Dong
Email: [email protected]
Institution: University of Michigan Department of Mechanical Engineering
ORCID:
Name: Jiankan Liao
Email: [email protected]
Institution: University of Michigan Department of Mechanical Engineering
ORCID: https://orcid.org/0000-0001-8104-8388
Name: Xun Huan
Email: [email protected]
Institution: University of Michigan Department of Mechanical Engineering
ORCID: https://orcid.org/0000-0001-6544-2764
Name: Daniel R Cooper
Email: [email protected]
Institution: University of Michigan Department of Mechanical Engineering
ORCID: https://orcid.org/0000-0003-2903-0468
Funding:
- National Science Foundation (NSF), under the Award Number 2040013
Nomenclature:
Key Points:
- The code supporting the paper "Expert elicitation and data noise learning for material flow analysis using Bayesian inference"
Research Overview:
We apply expert elicitation to assign informative prior to material flow analysis and conduct Bayesian inference for parameter and data noise learning.
Methodology:
We apply sequential Monte Carlo through the python pymc3 package to generate posterior samples to quantify MFA parametric uncertainty and data noise.
Instrument and/or Software specifications: python, pymc3 package for conducting Bayesian inference
Files contained here:
(1): Expert_Weighting.xlsx
- This speadsheet describes the weightings for expert intervieed for the elicitation
(2): inference.ipynb
-This file contains the code to conduct Bayesian inference to create the prior and posterior Sankey diagram in the manuscript
(3): Model-comparison.ipynb
-This file contains the code to perform Bayes factor estimation to select best performing model assumptions
(4): PDF_Fitting_Dirichlet.ipynb
-This file contains the code to fitg prior PDFs to the aggregated and weighted histograms from the experts
(5): Sankey_2012_data.xlsx
-This spreadsheet contains the underlying data used to construct the Sankey diagrams in Figures 5 and 6 of the article
Related publication(s): Dong, Jiayuan, Jiankan Liao, Xun Huan, and Daniel Cooper. "Expert elicitation and data noise learning for material flow analysis using Bayesian inference." Journal of Industrial Ecology 27, no. 4 (2023): 1105-1122.
Use and Access:
This data set is made available under an Attribution 4.0 International (CC BY 4.0).
To Cite Data:
Dong, Jiayuan, Jiankan Liao, Xun Huan, and Daniel Cooper. "Expert elicitation and data noise learning for material flow analysis using Bayesian inference." Journal of Industrial Ecology 27, no. 4 (2023): 1105-1122.