Process-based Bayesian Melding of Occupational Exposure Models and Industrial Workplace Data

In industrial hygiene a worker’s exposure to chemical, physical and biological agents is increasingly being modeled using deterministic physical models. However, predicting exposure in real workplace settings is challenging and approaches that simply regress on a physical model (e.g. straightforward non-linear regression) are less effective as they do not account for biases attributable, at least in part, to extraneous variability. This also impairs predictive performance. We recognize these limitations and provide a rich and flexible Bayesian hierarchical framework, which we call process-based Bayesian melding (PBBM), to synthesize the physical model with the field data. We reckon that the physical model, by itself, is inadequate for enhanced inferential performance and deploy (multivariate) Gaussian processes to capture extraneous uncertainties and underlying associations. We propose rich covariance structures for multiple outcomes using latent stochastic processes. We also pay attention to computational feasibility. In particular, we explore Markov chain Monte Carlo (MCMC) as well as Integrated Nested Laplace Approximation (INLA) to estimate PBBM parameters.