Bayesian modeling for physical processes in industrial hygiene using misaligned workplace data

João V D Monteiro, Sudipto Banerjee, Gurumurthy Ramachandran

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In industrial hygiene, a workers 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 misaligned. 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.

Original languageEnglish (US)
Pages (from-to)238-247
Number of pages10
Issue number2
StatePublished - Apr 3 2014


  • Bayesian melding
  • Cross-covariances
  • Gaussian processes
  • Linear ordinary differential equations
  • Markov chain Monte Carlo
  • Occupational exposure models


Dive into the research topics of 'Bayesian modeling for physical processes in industrial hygiene using misaligned workplace data'. Together they form a unique fingerprint.

Cite this