We discuss weighted least squares estimates of ill-conditioned linear inverse problems where weights are chosen to be inverse error covariance matrices. Least squares estimators are the maximum likelihood estimate for normally distributed data and parameters, but here we do not assume particular probability distributions. Weights for the estimator are found by ensuring its minimum follows a χ d...

The goal is to develop an inverse model capable of simultaneously estimating the parameters appearing in an air pollution model for an instantaneous point source, by using measured gas concentration data. The approach taken was to develop the inverse model as a non-linear least squares estimation problem in which the source term is estimated using measurements of pollution concentration on the ...