Bayesian State Space Modeling of Physical Processes in Industrial Hygiene.

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 article, we outline a flexible Bayesian framework for parameter inference and exposure prediction. In particular, we devise 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 accura

SUBMITTER: Abdalla N 

PROVIDER: S-EPMC7271698 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature