Bayesian State Space Modeling of Physical Processes in Industrial Hygiene

TL;DR

This paper introduces a Bayesian state space modeling framework for more accurate parameter inference and exposure prediction in industrial hygiene, accounting for noisy measurements and physical process uncertainties.

Contribution

It develops a flexible Bayesian approach using state space models and Monte Carlo methods to improve inference in physical exposure models with noisy data.

Findings

Bayesian models outperform deterministic models in noisy settings

Monte Carlo methods effectively estimate parameters in nonlinear models

Framework validated on simulated and laboratory data

Abstract

Exposure assessment models are deterministic models derived from physical-chemical laws. In real workplace settings, chemical concentration measurements can be noisy and indirectly measured. In addition, inference on important parameters such as generation and ventilation rates are usually of interest since they are difficult to obtain. In this paper we outline a flexible Bayesian framework for parameter inference and exposure prediction. In particular, we propose using Bayesian state space models by discretizing the differential equation models and incorporating information from observed measurements and expert prior knowledge. At each time point, a new measurement is available that contains some noise, so using the physical model and the available measurements, we try to obtain a more accurate state estimate, which can be called filtering. We consider Monte Carlo sampling methods for parameter estimation and inference under nonlinear and non-Gaussian assumptions. The performance of the different methods is studied on computer-simulated and controlled laboratory-generated data. We consider some commonly used exposure models representing different physical hypotheses.