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We consider a network of sensors that measure the intensities of a complex plume composed of multiple absorption–diffusion source components. We address the problem of estimating the plume parameters, including the spatial and temporal source origins and the parameters of the diffusion model for each source, based on a sequence of sensor measurements. The approach not only leads to multiple-source detection, but also the characterization and prediction of the combined plume in space and time. The parameter estimation is formulated as a Bayesian inference problem, and the solution is obtained using a Markov chain Monte Carlo algorithm. The approach is applied to a simulation study, which shows that an accurate parameter estimation is achievable. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Smoothing spline estimators are considered for inference in varying-coefficient models with one effect modifying covariate. Bayesian ‘confidence intervals’ are developed for the coefficient curves and efficient computational methods are derived for computing the curve estimators, fitted values, posterior variances and data-adaptive methods for selecting the levels of smoothing. The efficacy and utility of the methodology proposed are demonstrated through a small simulation study and the analysis of a real data set. [ABSTRACT FROM AUTHOR]

Presents a study that examined an algorithm that can be used to compute a smoothing spline of any given degree. Theoretical background; Methodology; Results and conclusions.

Examines the boundary corrected cubic smoothing spline. Production of a uniformly fourth order estimator through boundary corrected cubic smoothing spline; Measurement of the estimator through an Order n algorithm; Capability of boundary corrected estimator in improving estimation efficiency in finite samples.

A response by Chunfeng Huang and colleagues to a letter to the editor about their article "Bayesian source detection and parameter estimation of a plume model based on sensor network measurements" in the previous issue is presented.