A Bayesian approach to forecasting daily air-pollutant levels

摘要

Forecasting air-pollutant levels is an important issue, due to their adverse effects on public health, and often a legislative necessity. The advantage of Bayesian methods is their ability to provide density predictions which can easily be transformed into ordinal or binary predictions given a set of thresholds. We develop a Bayesian approach to forecasting PMpredictionswhere experts already base their predictions on predictions from a statistical model. A Bayesian approachespecially using Gaussian processesoffers several advantages: superior performance, robustness to overfitting, more information, and the ability to efficiently adapt to different cost matrices.