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log likelihood in Chinese

Pronunciation:
How to pronounce "log likelihood""log likelihood" in a sentence

Translationmobile phoneMobile

  • 對(duì)數(shù)可能性

Examples

  • Log likelihood ratio decoding of ldpc coded modulation in ofdm system
    編碼調(diào)制的對(duì)數(shù)似然比譯碼
  • Log likelihood function
    對(duì)數(shù)概似函數(shù)
  • In chapter l , we introduce the relative background on this paper and give some simple expressions of the work which have been studied . in chapter 2 , in virtue of the notion of likelihood ratio the limit properties of the sequences of dependent nonnegative continuous random variables are studied , and a class of strong limit theorems represented by inequalities are obtained . the bounds given by these theorems depend on positive constant c . in chapter 3 , by means of the notion of log likelihood ratio , a kind random strong deviation theorem are obtained , and the bounds given by these theorems depend on r ( )
    第一章，介紹本論文的選題背景，對(duì)已有的工作進(jìn)行扼要的介紹；第二章，利用似然比的概念研究相依連續(xù)型非負(fù)隨機(jī)變量序列的極限性質(zhì)，得到一類強(qiáng)偏差定理，其偏差界依賴于正常數(shù)c ；第三章，利用對(duì)數(shù)似然比的概念得到一類隨機(jī)偏差定理，其偏差界依賴于r ( ) ，證明中引進(jìn)了尾概率和尾概率的laplace變換的概念；第四章，利用對(duì)數(shù)似然比的概念，得到了一類關(guān)于任意連續(xù)型隨機(jī)變量序列的泛函的強(qiáng)偏差定理。
  • In this paper , by means of the notion of likelihood ratio and log likelihood ratio the limit properties of the sequences of dependent continuous random variables are studied , and a class of strong limit theorems represented by inequalities are obtained . in the proof an approach of applying the tool of laplace transform to the study of strong limit theorem is proposed
    本論文繼續(xù)這方面的工作，利用似然比、對(duì)數(shù)似然比的概念研究相依連續(xù)型隨機(jī)變量序列的極限性質(zhì)，得到相應(yīng)的用不等式表示的強(qiáng)偏差定理。證明中提出了將laplace變換的工具應(yīng)用于強(qiáng)極限定理研究的一種方法。
  • In chapter 4 , the purpose of this chapter is to establish a kind of strong deviation theorems of functional for the sequences of arbitrary continuous random variables , by using the conception of log likelihood ratio , and extend the strong deviation theorems on the differential entropy for dependent arbitrary continuous information sources on the the probability space ( , . f , p )
    使得對(duì)于在概率空間( ， f ， p )上的任意連續(xù)型信源的微分熵的強(qiáng)偏差定理是本文的推論；第五章，總結(jié)本文的主要結(jié)論。

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