In this paper, we (re)introduce the independent gamma prior for penalized likelihood reconstruction for both emission and transmission tomography. We give the expressions for bias and variance, and validate the expressions using Monte Carlo simulations. We also test a plug-in approach to get an approximate estimate of the variance image. We list the fixed point local point-spread function expre...

Abstract — To improve transparency and reduce the curse of dimensionality of non–linear black–box models, the local modelling approach was proposed. Poor transient response of Local Model networks led to the use of non–parametrical probabilistic models such as the Gaussian Process prior approach. Recently, Gaussian Process models were applied in the Minimum Variance Control. This paper introduc...

The LIDAR (LIght Detection And Ranging) technique is an eecient tool for remote monitoring of the distribution of a number of atmospheric species. We study measurements of SO 2 emitted from the Italian vulcano Etna. In this study we focus on the treatment of data and on the procedure to evaluate range resolved concentrations. In order to make an in-depth analysis we prepared the lidar system to...

This paper proposes and analyzes a Running Variance Metric performance measurement of wireless local area networks and its formal aspects. Our approach evaluates the performance of wireless local area networks in infrastructure mode as well as in ad hoc mode. The Running Variance Metric is used to discover relative traffic loads of available accesspoints/gateways at the network layer in order t...

We propose a method for medical image denoising using calculus of variations and local variance estimation by shaped windows. This method reduces any additive noise and preserves small patterns and edges of images. A pyramid structure-texture decomposition of images is used to separate noise and texture components based on local variance measures. The experimental results show that the proposed...

We introduce a new version of Stein’s method that reduces a large class of normal approximation problems to variance bounding exercises, thus making a connection between central limit theorems and concentration of measure. Unlike Skorokhod embeddings, the object whose variance has to be bounded has an explicit formula that makes it possible to carry out the program more easily. As an applicatio...

There are a number of estimators of a long-memory process' long-memory parameter when the parameter is assumed to hold constant over the entire data set, but currently no estimator exists for a time-varying long-memory parameter. In this paper we construct an estimator of the time-varying long-memory parameter that is based on the time-scale properties of the wavelet transform. Because wavelets...

Since landscape attributes show different patterns at different spatial extents, it is fundamental to identify how the relation between landscape structure and plant species diversity at local scale varies with scale. Then, it is fundamental to assess the appropriate extent at which landscape factors affect plant species richness at the local scale. To investigate this relation, data on plant s...