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kernel density in Chinese

Pronunciation:
How to pronounce "kernel density""kernel density" in a sentence

Translationmobile phoneMobile

  • 籽粒容量

Examples

  • Moderate deviations for the kernel density estimators
    核密度估計(jì)的中偏差
  • Methods non - parameter kernel density estimation method was adopted
    方法采用非參數(shù)核密度估計(jì)推斷方法。
  • The general class of kernel density estimates for positive associated samples
    樣本下一般形式的密度估計(jì)
  • Kernel density estimation of species abundance distribution in rare and endangered castanopsis kawakamii natural forest
    格氏栲天然林物種多度分布的核估計(jì)研究
  • As not to know the stock prices obey what distribution , we accord to historical data to estimate the distribution of the ultimate stock prices by kernel density estimation , then develop the theorems for options pricing , and price the option
    本文研究股票價(jià)格不服從幾何布朗運(yùn)動(dòng),即股票的對(duì)數(shù)收益率并不服從正態(tài)分布時(shí)的歐式期權(quán)價(jià)值評(píng)估的非參數(shù)估計(jì)
  • In last chapter , a new conception and model for var , based on prediction are brought forward . finally , a kind of new kernel density estimating function , adapting to financial time series is employed to extend time series kernel density estimating model
    文中最后一部分，從風(fēng)險(xiǎn)價(jià)值預(yù)測(cè)的角度出發(fā)，建立了基于var預(yù)測(cè)的概念和模型，提出了一種適合估計(jì)金融時(shí)間序列分布的核密度函數(shù)，并采用加權(quán)法推廣了時(shí)間序列核密度估計(jì)模型
  • The work in this thesis is based on three technologies of multivariable statistical process control ( mspc ) , the principal component analysis ( pca ) , the partial least squares ( pls ) and the kernel density estimate ( kde ) . the work involves the following contents
    基于多元統(tǒng)計(jì)過(guò)程控制方法中的主元分析法，偏最小二乘法和核函數(shù)分析法這三種技術(shù)，本課題主要研究了以下內(nèi)容： 1 )用面向?qū)ο蟮姆椒ㄩ_發(fā)多元統(tǒng)計(jì)過(guò)程控制狀態(tài)監(jiān)測(cè)應(yīng)用系統(tǒng)。
  • This paper investigates the application of the multivariate statistical process monitoring and control technology , which employs both multiway principal component analysis ( mpca ) and kernel density estimation ( kde ) , to real time status monitoring and fault diagnosis of batch production processes
    本文主要研究了運(yùn)用多向主元分析法和核函數(shù)法概率密度估計(jì)相結(jié)合的多元統(tǒng)計(jì)過(guò)程監(jiān)控技術(shù)對(duì)間歇生產(chǎn)過(guò)程進(jìn)行實(shí)時(shí)的狀態(tài)監(jiān)測(cè)與故障診斷。
  • One is the bss based on kernel density estimation ( kde ) and genetic algorithm ( ga ) , the other is the blind deconvolution based on high order cross cumulants and ga . without nlf , the performance of separation in both algorithms is independent with the kurtosis of the sources
    兩種算法的實(shí)現(xiàn)無(wú)需引入非線性函數(shù),因此都與源信號(hào)的峭度性質(zhì)無(wú)關(guān);另外,選取全局搜索的遺傳算法進(jìn)行尋優(yōu),避免了梯度法搜索的局部性,使得算法均能收斂到問(wèn)題的全局最優(yōu)解。
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