可憎
发表于 2025-3-28 18:34:54
Karen Trimmer,Tara Newman,Fernando F. Padróing spatial covariances between monitored locations and unmonitored locations which combines the information from the observed station-pair covariances with a fitted stationary model for spatial covariance. The principal motivation is to obtain interpolation precision estimates which reflect local covariance properties.
progestin
发表于 2025-3-28 21:38:09
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推测
发表于 2025-3-29 02:11:48
,Enrico Fermi’s scientific work,with a view to highlighting the progress made to date. The basic techniques — from the preliminary borehole valuation of mining properties to the block estimating of ore reserves and foreseeable recoveries — are analysed and the prospects for methodological improvements are discussed. The techniques
defile
发表于 2025-3-29 04:37:36
The Ethical Dimension of Economics,o a question concerning the consistency of these models: for a given system of bivariate distributions, does a random function which could be compatible with this system, really exist? Going further, which class of covariances is compatible with a given univariate distribution? The condition under w
变白
发表于 2025-3-29 10:57:02
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Calibrate
发表于 2025-3-29 12:30:19
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闯入
发表于 2025-3-29 17:05:02
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Camouflage
发表于 2025-3-29 20:32:23
Sanjiv Kumar,Mehryar Mohri,Ameet Talwalkarween successive indicators. These residuals are precisely the factors of this model. This leads to a simple expression for indicator cokriging, i.e. for Disjunctive Kriging..In this model, the bivariate distribution law is determined from the marginal law and the law of the minimum of the two variab
真繁荣
发表于 2025-3-30 00:27:06
Employment Prospects for Minority Womenbe known, the latter assumes nothing about the parameters involved in the expected function. The Bayesian approach allows the user to specify prior knowledge about model parameters as a qualified guess with uncertainties. This may be done in cases where the parameters have physical meaning. Two vers
细胞膜
发表于 2025-3-30 05:20:09
Karen Trimmer,Tara Newman,Fernando F. Padródata the observed covariances calculated from pairs of time series are often highly non-stationary geographically. A proposal is presented for estimating spatial covariances between monitored locations and unmonitored locations which combines the information from the observed station-pair covariance