'Bayesian source detection and parameter estimation of a plume model based on sensor network measurements' by C. Huang et al.: Discussion 3

摘要

The underlying problem addressed by this paper is the interpretation of data from a sensor network, which in this specific case is a sensor network intended to detect and track one or more plumes of hazardous material released by an explosion or accident. A data analyst with a background in data mining or machine learning would likely prefer to treat this as a problem of building a predictive model from training data. In other words, if data were available from sample plume releases, then the analyst would try to extract features from the data and use it to build a model that could predict the origin of the plume and its evolution over time. This would be a strictly data-driven approach that uses little if any of the underlying physical knowledge of how plumes of material disperse throughout the surrounding region. (However, such models might well apply techniques that take into account the spatio-temporal nature of the domain.) Given enough training data, there are significant advantages to such an approach in that the actual physics of a situation is often hard to model and thus simulation or analytical models of such phenomena are often difficult to construct and are only approximate.