Abdalla Nada, Banerjee Sudipto, Ramachandran Gurumurthy, Arnold Susan
Department of Biostatistics, University of California-Los Angeles, Los Angeles, CA.
Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Technometrics. 2020;62(2):147-160. Epub 2019 Jul 22.
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 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. Supplementary materials for this article are available online.
暴露评估模型是基于物理化学定律推导出来的确定性模型。在实际工作场所环境中，化学物质浓度测量可能存在噪声且为间接测量。此外，诸如生成速率和通风速率等重要参数的推断通常备受关注，因为这些参数难以获取。在本文中，我们概述了一个用于参数推断和暴露预测的灵活贝叶斯框架。特别地，我们通过离散微分方程模型并纳入来自观测测量和专家先验知识的信息来设计贝叶斯状态空间模型。在每个时间点，会有一个包含一些噪声的新测量值，因此利用物理模型和可用测量值，我们试图获得更准确的状态估计，这可称为滤波。我们考虑在非线性和非高斯假设下用于参数估计和推断的蒙特卡罗抽样方法。在计算机模拟和受控实验室生成的数据上研究了不同方法的性能。我们考虑了一些代表不同物理假设的常用暴露模型。本文的补充材料可在线获取。
