A time dependent Bayesian nonparametric model for air quality analysis

摘要: Air quality monitoring is based on pollutants concentration levels, typically recorded in metropolitan areas. These exhibit spatial and temporal dependence as well seasonality trends, their analysis demands flexible robust statistical models. Here we propose to model the measurements of particulate matter, composed by atmospheric carcinogenic agents, means a Bayesian nonparametric dynamic which accommodates structures present data allows for fast efficient posterior computation. Lead need infer probability threshold crossing at arbitrary time points, crucial contingency decision making, apply time-varying density estimation PM 2.5 dataset collected Santiago, Chile, analyze various other quantities interest derived from estimate.