A. Hanea and W. Harrington
Ordinal Data Mining for Fine Particles with Non
Parametric Continuous Bayesian Belief Nets
We
introduce a Bayesian Belief Net (BBN) based approach for analysing
the relationship between SO2
emissions, and concentrations of fine particulate matter, PM2.5. PM2.5
exposure has adverse health effects, hence we study this
relationship with the goal of quantifying the health benefits of emission
reductions. The main advantage of our approach is that it can handle a large
number of continuous variables, without making any assumptions about their
marginal distributions, in a fast manner. Rapid computations are of little
value if the model itself cannot be validated. We discuss the issue of
validation and additionally perform backward and forward inference.
КЛЮЧЕВЫЕ СЛОВА: Bayesian Belief Net, validation, health effects