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Activity Number: 166 - SPEED: Topics in Bayesian Analysis
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #327084 Presentation
Title: Bayesian Forecasting and Dynamic Modeling of Physical Processes in Industrial Hygiene
Author(s): Nada Abdalla* and Sudipto Banerjee and Gurumurthy Ramachandran and Susan Arnold
Companies: UCLA and UCLA School of Public Health and John Hopkins University and University of Minnesota
Keywords: Kalman filter; Particle filter; MCMC; Nonlinear; Non-Gaussian; State space models

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 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.

Authors who are presenting talks have a * after their name.

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