Inverse problems in geophysics require the introduction of complex a priori information and are solved using computationally expensive Monte Carlo techniques where large portions of the model space are explored . The geostatistical method allows for fast integration of complex a priori information in the form of covariance functions and training images. We combine geostatistical methods and inv...

This paper develops a simulation-based approach to sequential parameter learning and filtering in general state-space models. Our methodology is based on a rolling-window Markov chain Monte Carlo (MCMC) approach and can be easily implemented by modifying state-space smoothing algorithms. Furthermore, the filter avoids the degeneracies that hinder particle filters and is robust to outliers. We i...

In this paper an adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented. The capability of the proposed method (we named ANFIS2) to function approximation and dynamical system identification is shown. The ANFIS2 structure ...

This paper addresses the issue of modelling the uncertainty about the value of continuous soil Ž . attributes, at any particular unsampled location local uncertainty as well as jointly over several Ž . locations multiple-point or spatial uncertainty . Two approaches are presented: kriging-based and Ž . simulation-based techniques that can be implemented within a parametric e.g. multi-Gaussian o...

in this paper, the detector of moving target in mimo radar with widely separated antenna in the presence of α-stable clutter is proposed. due to the complexity of unknown parameter estimation in the presence of target, the rao test is applied. the α-stable model is a general model that can applied to different environments with appropriate selection of parameters. the problem with this model is...

In this paper, a new class of Gaussian processes is proposed for resource-constrained mobile sensor networks. Such a Gaussian process builds on a GMRFwith respect to a proximity graph over a surveillance region. The main advantages of using this class of Gaussian processes over standard Gaussian processes defined by mean and covariance functions are its numerical efficiency and scalability due ...

Truncated Gaussian simulation (TGS) and plurigaussian simulation (PGS) are widely accepted methods for generating realisations of geological domains (lithofacies) that reproduce contact relationships. The realisations can be used to evaluate transfer functions related to the lithofacies occurrence, the simplest ones of which are the probability of occurrence of each lithofacies and the most pro...

As an example for the fast calculation of distributional parameters Gaussian processes, a new Monte Carlo algorithm computation quantiles supremum norm weighted Brownian bridges is proposed. it known, corresponding distributions arise asymptotically CUSUM statistics change-point detection. The employs adaptive (sequential) time discretization trajectories bridge. A simulation study shows that b...

A voice quality prediction method based on Gaussian mixture models (GMMs) is improved by constructing a feature selection algorithm to provide the best GMMbased prediction quality. The proposed sequential selection algorithm performs N -survivor search, allowing for trading between design complexity and performance. Simulation shows that predictors designed using the proposed algorithm outperfo...