The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Robert Berger - One of the best experts on this subject based on the ideXlab platform.
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cumulant expansion for fast estimate of non condon effects in vibronic transition profiles
Scientific Reports, 2017Co-Authors: Robert BergerAbstract:When existing, Cumulants can provide valuable information about a given distribution and can in principle be used to either fully reconstruct or approximate the parent distribution function. A previously reported cumulant expansion approach for Franck–Condon profiles [Faraday Discuss., 150, 363 (2011)] is extended to describe also the profiles of vibronic transitions that are weakly allowed or forbidden in the Franck–Condon approximation (non-Condon profiles). In the harmonic approximation the Cumulants of the vibronic profile can be evaluated analytically and numerically with a coherent state-based generating function that accounts for the Duschinsky effect. As illustration, the one-photon 1 1Ag → 1 1B2u UV absorption profile of benzene in the electric dipole and (linear) Herzberg–Teller approximation is presented herein for zero Kelvin and finite temperatures.
Alain Giron - One of the best experts on this subject based on the ideXlab platform.
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Cumulants of multiinformation density in the case of a multivariate normal distribution
Statistics & Probability Letters, 2020Co-Authors: Guillaume Marrelec, Alain GironAbstract:Abstract We consider a generalization of information density to a partitioning into N ≥ 2 subvectors. We calculate its cumulant-generating function and its Cumulants in the particular case of a multivariate normal distribution, showing that these quantities are only a function of all the regression coefficients associated with the partitioning.
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Cumulants of multiinformation density in the case of a multivariate normal distribution
Statistics and Probability Letters, 2020Co-Authors: Guillaume Marrelec, Alain GironAbstract:We consider a generalization of information density to a partitioning into N ≥ 2 subvectors. We calculate its cumulant-generating function and its Cumulants, showing that these quantities are only a function of all the regression coefficients associated with the partitioning
Roussos Dimitrakopoulos - One of the best experts on this subject based on the ideXlab platform.
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a new approach for geological pattern recognition using high order spatial Cumulants
Computers & Geosciences, 2010Co-Authors: Hussein Mustapha, Roussos DimitrakopoulosAbstract:Spatially distributed natural phenomena represent complex non-linear and non-Gaussian systems. Currently, their spatial distributions are typically studied using second-order spatial statistical models, which are limiting considering the spatial complexity of natural phenomena such as geological applications. High-order geostatistics is a new area of research based on higher-order spatial connectivity measures, especially spatial Cumulants as suitable for non-Gaussian and non-linear phenomena. This paper presents HOSC or High-order spatial Cumulants, an algorithm for calculating spatial Cumulants, including anisotropic experimental Cumulants based on spatial templates. High-order Cumulants are calculated on two- and three-dimensional synthetic training images so as to elaborate on their characteristics. Spatial Cumulants up to and including the fifth-order are found to be efficient in characterizing patterns on both binary and continuous images. The behaviour of spatial Cumulants is shown to characterize well the behaviour of the spatial architecture of geological data, including the degree of homogeneity and connectivity. The high-order Cumulants are found to be relatively insensitive to the number of data used, and relatively small data sets are sufficient to provide cumulant maps. HOSC has been coded in FORTAN 90 and is easily integrated to the S-GeMS open source platform.
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high order statistics of spatial random fields exploring spatial Cumulants for modeling complex non gaussian and non linear phenomena
Mathematical Geosciences, 2010Co-Authors: Roussos Dimitrakopoulos, Hussein Mustapha, Erwan GloaguenAbstract:The spatial distributions of earth science and engineering phenomena under study are currently predicted from finite measurements and second-order geostatistical models. The latter models can be limiting, as geological systems are highly complex, non-Gaussian, and exhibit non-linear patterns of spatial connectivity. Non-linear and non-Gaussian high-order geostatistics based on spatial connectivity measures, namely spatial Cumulants, are proposed as a new alternative modeling framework for spatial data. This framework has two parts. The first part is the definition, properties, and inference of spatial Cumulants—including understanding the interrelation of cumulant characteristics with the in-situ behavior of geological entities or processes, as examined in this paper. The second part is the research on a random field model for simulation based on its high-order spatial Cumulants.
Guillaume Marrelec - One of the best experts on this subject based on the ideXlab platform.
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Cumulants of multiinformation density in the case of a multivariate normal distribution
Statistics & Probability Letters, 2020Co-Authors: Guillaume Marrelec, Alain GironAbstract:Abstract We consider a generalization of information density to a partitioning into N ≥ 2 subvectors. We calculate its cumulant-generating function and its Cumulants in the particular case of a multivariate normal distribution, showing that these quantities are only a function of all the regression coefficients associated with the partitioning.
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Cumulants of multiinformation density in the case of a multivariate normal distribution
Statistics and Probability Letters, 2020Co-Authors: Guillaume Marrelec, Alain GironAbstract:We consider a generalization of information density to a partitioning into N ≥ 2 subvectors. We calculate its cumulant-generating function and its Cumulants, showing that these quantities are only a function of all the regression coefficients associated with the partitioning
Peter T. Kim - One of the best experts on this subject based on the ideXlab platform.
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CONSISTENT ESTIMATION OF THE FOURTH‐ORDER CUMULANT SPECTRAL DENSITY
Journal of Time Series Analysis, 2008Co-Authors: Peter T. KimAbstract:Abstract. In this paper we consider the estimation of the fourth-order cumulant spectral density. Indeed this is the first case where the cumulant depends on lower-order product moments for a mean-zero stationary process. The proposed estimator of the fourth-order cumulant spectral density is constructed by replacing product moments with appropriately weighted estimates of product moments according to the definition of the fourth-order cumulant spectral density. Asymptotic unbiasedness and consistency are shown to hold for these estimators under stationarity and absolute summability of Cumulants up to various orders with no restrictions on the frequencies. An expression for the asymptotic variance is also obtained.
