Bayesian inference for source determination with applications to a complex urban environment

324 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Locating and quantifying gas emission sources using remotely obtained concentration data (2012, background): “r parameters of interest and quanties the uncertainty in the estimated parameter, whereas the latter provides point estimates for the parameters of interest through optimising an objective function. Keats et al. (2007) note that determining the source of an emission from the limited information prov”
p-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data (2016, background): “Previous research on the air pollution cause identiﬁcation mostly relies on chemical receptor [15] or dispersion models [14].”
Source Inversion for Contaminant Plume Dispersion in Urban Environments Using Building-Resolving Simulations (2005, background): “The exact direct-inversion approaches are usually limited to processes governed by linear equations and also generally assume the system is steady state (Errico 1997; Enting 2002; Keats et al. 2007 ).”