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MULTIVARIATE

  • sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a G

    sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing MULTIVARIATE data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Windows binary: sbgcop_0.95.zip

    標(biāo)簽: Semiparametric estimation parameters estimates

    上傳時(shí)間: 2016-04-15

    上傳用戶:qilin

  • sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a G

    sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing MULTIVARIATE data. Version: 0.95 Date: 2007-03-09 Author: Peter Hoff Maintainer: Peter Hoff <hoff at stat.washington.edu> License: GPL Version 2 or later URL: http://www.stat.washington.edu/hoff CRAN checks: sbgcop results Downloads: Reference manual: sbgcop.pdf

    標(biāo)簽: Semiparametric estimation parameters estimates

    上傳時(shí)間: 2014-12-08

    上傳用戶:一諾88

  • New training algorithm for linear classification SVMs that can be much faster than SVMlight for larg

    New training algorithm for linear classification SVMs that can be much faster than SVMlight for large datasets. It also lets you direcly optimize MULTIVARIATE performance measures like F1-Score, ROC-Area, and the Precision/Recall Break-Even Point.

    標(biāo)簽: classification algorithm for training

    上傳時(shí)間: 2014-12-20

    上傳用戶:stvnash

  • gibbs抽樣 matlab實(shí)現(xiàn)

    使用matlab實(shí)現(xiàn)gibbs抽樣,MCMC: The Gibbs Sampler  多元高斯分布的邊緣概率和條件概率  Marginal and conditional distributions of MULTIVARIATE normal distribution

    標(biāo)簽: matlab gibbs 抽樣

    上傳時(shí)間: 2019-12-10

    上傳用戶:real_

  • wavelet and field forecast verification

    Current field forecast verification measures are inadequate, primarily because they compress the comparison between two complex spatial field processes into one number. Discrete wavelet transforms (DWTs) applied to analysis and contemporaneous forecast fields prove to be an insightful approach to verification problems. DWTs allow both filtering and compact physically interpretable partitioning of fields. These techniques are used to reduce or eliminate noise in the verification process and develop MULTIVARIATE measures of field forecasting performance that are shown to improve upon existing verification procedures.

    標(biāo)簽: field forecast verification

    上傳時(shí)間: 2020-07-22

    上傳用戶:

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