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Bayesian Modeling for Physical Processes in Industrial Hygiene Using Misaligned Workplace Data

Summary: [This abstract is based on the authors' abstract.] 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 is usually misaligned. This means that not all timepoints measure concentrations near and far from the source. To address these challenges, this article outlines a flexible Bayesian hierarchical framework to synthesize the physical model with the field data. It is reckoned that the physical model, by itself, is inadequate for enhanced inferential and predictive performance, so (multivariate) Gaussian processes are deployed to capture uncertainties and associations. The authors propose rich covariance structures for multiple outcomes using latent stochastic processes. This article has supplementary material available online.

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  • Topics: Statistics, Engineering
  • Keywords: Bayesian methods, Industrial engineering, Hierarchical systems, Physical modeling, Covariance, Stochastic models, Multivariate analysis, Markov chains, Monte Carlo methods, Gaussian curve
  • Author: Monteiro, João V.D.; Banerjee, Sudipto; Ramachandran, Gurumurthy;
  • Journal: Technometrics