Details:

Alternative Title:
Technometrics
Personal Author:
Monteiro, João V. D. ; Banerjee, Sudipto ; Ramachandran, Gurumurthy
Monteiro, João V. D. ; Banerjee, Sudipto ; Ramachandran, Gurumurthy Less -
Description:
In industrial hygiene, a worker's exposure to chemical, physical, and biological agents is increasingly being modeled using deterministic physical models that study exposures near and farther away from a contaminant source. However, predicting exposure in the workplace is challenging and simply regressing on a physical model may prove ineffective due to biases and extraneous variability. A further complication is that data from the workplace are usually |. This means that not all timepoints measure concentrations near and far from the source. We recognize these challenges and outline a flexible Bayesian hierarchical framework to synthesize the physical model with the field data. We reckon that the physical model, by itself, is inadequate for enhanced inferential and predictive performance and deploy (multivariate) Gaussian processes to capture uncertainties and associations. We propose rich covariance structures for multiple outcomes using latent stochastic processes. This article has supplementary material available online.
Subject:
[+]
Article
Bayesian Melding
Cross-covariances
Gaussian Processes
Linear Ordinary Differential Equations
Markov Chain Monte Carlo
Occupational Exposure Models

Source:
Technometrics. 56(2)
Pubmed ID:
32336793
Pubmed Central ID:
PMC7180385
Document Type:
Journal Article
Funding:
R01 OH010093/OH/NIOSH CDC HHS/United States
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:17c296ec99c2bcda810bda36ea099152665006d6301e35e323e2bb9a70f29f5b
File Type:

Supporting Files

• 	NIHMS1007088-supplement-Identifiability.pdf	pdf
  	NIHMS1007088-supplement-ODE.pdf	pdf
  	NIHMS1007088-supplement-Priors.pdf	pdf
  	NIHMS1007088-supplement-Rcode1.r	bin
  	NIHMS1007088-supplement-Rcode2.r	bin
  	NIHMS1007088-supplement-README.txt	txt
  	nihms-1007088-f0001.gif	gif
  	nihms-1007088-f0001.jpg	jpeg
  	nihms-1007088-f0002.gif	gif
  	nihms-1007088-f0002.jpg	jpeg
  	nihms-1007088-f0003.gif	gif
  	nihms-1007088-f0003.jpg	jpeg
  	nihms-1007088.nxml	xml
  	NIHMS1007088-supplement-Additional_Analysis.pdf	pdf

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