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

h
Attribute Value
Methodology
  • We apply sequential Monte Carlo through the python pymc3 package to generate posterior samples to quantify MFA parametric uncertainty and data noise.
Description
  • We apply expert elicitation to assign informative prior to material flow analysis and conduct Bayesian inference for parameter and data noise learning.
Creator
Depositor
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
ORSP grant number
  • 2040013
Keyword
Date coverage
  • 2012
Citations to related material
  • 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.
Related items in Deep Blue Documents
Resource type
Last modified
  • 04/18/2025
Published
  • 04/18/2025
Language
DOI
  • https://doi.org/10.7302/dz6c-5w53
License
To Cite this Work:
Dong, J., Liao, J., Huan, X., Cooper, D. R. (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

Relationships

This work is not a member of any user collections.

Files (Count: 6; Size: 375 KB)

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.

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