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probability measure中文是什么意思

  • 概率測度
  • 概率度量

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  • 例句與用法
  • Statistical convergence and probability measure
    統(tǒng)計收斂與概率測度
  • With respect to the probability measures
    關(guān)于概率族
  • Changes of probability measure and options pricing in jump - diffusion models
    跳擴散模型中的測度變換與期權(quán)定價
  • Equivalence theorem about weak convergence of probability measures ' convolution powers on locally compact groups
    局部緊群上概率測度卷積冪弱收斂等價性定理
  • Limit theorems for the integration of function sequence with respect to weak convergence probability measure sequence
    函數(shù)序列關(guān)于弱收斂概率測度序列積分的極限定理
  • Particularly , if the probability measure u is symmetric or if the mean value is 0 , then u is quasi - symmetric
    特別地,當(dāng)概率測度對稱或均值為零時,此概率測度是擬對稱的。
  • By using time - risk discount method , it is possible to price general assets under real probability measure , and the price expression is given
    利用時間風(fēng)險折現(xiàn)方法實現(xiàn)了風(fēng)險資產(chǎn)在實際測度下的定價,并給出其具體的價格表達式。
  • This paper develops a continuous time model by means of the bsde methodology , in order to price risky assets in terms of the real probability measure
    摘要本文利用倒向隨機微分方程研究了連續(xù)時間下基于可交易證券的風(fēng)險資產(chǎn)定價模型。
  • Secondly we study the properties of moment generating functions of probability measures , calculate its sub - differential by the convex analysis , use it to characterize the quasi - symmetric probability
    本文研究了概率測度的矩母函數(shù)的性質(zhì),用凸分析的方法算出了矩母函數(shù)的次微分,并用此完全刻畫了c . j . stone提出的擬對稱概率測度的性質(zhì)。
  • We look at the problem of learning from examples as the problem of multivariate function approximation from sparse chosen data , and then consider the case in which the data are drawn , instead of chosen , according to a probability measure
    并檢視稀疏精選值中多變量函數(shù)近似法等這些從實例學(xué)習(xí)法所發(fā)現(xiàn)的問題,然后根據(jù)機率衡量,審思隨機獲得資料而非選定資料的案例。
  • 更多例句:  1  2
  • 百科解釋
In mathematics, a probability measure is a real-valued function defined on a set of events in a probability space that satisfies measure properties such as countable additivity.An introduction to measure-theoretic probability by George G.
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