unknown variance 中文意思是什麼
unknown variance
解釋
未知方差-
For a general linear model ( input matrix is deterministic ), under a certain conditions on variance matrix invertibility, the two estimates can be identical provided that they have the same priori information on the parameter under estimation. even if the above information is unknown only for the optimally weighted ls estimate, the sufficient condition and necessary condition, under which the two estimates are identical, is derived. more significantly, we know how to design input of the linear system to make the performance of the optimally weighted ls estimation identical to that of the linear minimum variance estimation in case of being lack of prior information
在一般線性模型（即輸入矩陣為確定性）下，當兩種估計都利用有關被估參數的先驗信息時，二者在方差陣可逆的一定條件下可達到一致；當最優加權最小二乘估計不利用此先驗信息時，存在二者一致的充分條件和必要條件，進而找到一種設計輸入矩陣的方法，使得在先驗信息缺乏的條件下，仍可利用最優加權最小二乘估計達到與線性最小方差估計一樣優越的估計性能。 -
Unlike the normal two stages estimate method ( the usual nonparametric weighted method combined with the least square estimate ), considering the characteristics of this model, this paper uses the least square estimate combining with the usual nonparametric weighted method and defines the estimators and n2 for the unknown parameter, the unkown fuction g ( ) and the unknown variance of errors 2
與通常採用的兩階段估計方法即非參數權函數法結合最小二乘法不同，考慮到此模型本身的特性，本文採用最小二乘法結合一般非參數權函數估計方法，定義了未知待估參數和未知函數g （ ? ）及誤差方差~ 2的估計量（ ? ） _ n ， （ ? ） _ n （ ? ）和（ -
Application of statistical. estimation of a mean unknown variance
統計學應用.平均值的計算未知方差 -
Application of statistical. comparaison of a mean with a given value unknown variance
統計學應用.給定值與平均值的比較未知方差 -
( 3 ) how to design the bayesian test method about the parameter ' s linear hypothesis according to the relationship between the multivariate t distribution and f distribution. ( 4 ) the bayesian diagnosis and unit root test method about the random error series. ( 5 ) the bayesian mean value quality control chart when the variance is known and the mean value - standard error control chart when the variance is unknown
然後，研究了擴散先驗分佈下單方程模型參數的貝葉斯估計理論，證明了模型系數的后驗分佈為多元t分佈，模型誤差項方差的后驗估計為逆gamma分佈；根據多元t分佈和f分佈之間的關系，構造了模型系數線性假設檢驗的貝葉斯方法；根據hpd置信區間構造了隨機誤差序列自相關的貝葉斯診斷和單位根檢驗方法，並利用單方程模型的貝葉斯推斷理論研究了方差已知時的貝葉斯均值控制圖和方差未知時的貝葉斯均值?標準差控制圖。 -
The basic idea of the estimate method is, firstly, based on the linear model yi = x ' i + ei, defining the least square estimator n of the linear model for the unkown parametric ; secondly, using the estimator n we " ve got to substitute for in the original semiparametric regression model yi = x ' i + g ( xi ) + ei and using the usual nonparametric weighted function method to define the estimator gn ( - ) for the unknown function g ( ) ; finally, defining the estimator 2 for the unknown variance of errors 2
其估計方法的基本思路是先基於線性模型y _ i = x _ i + e _ i ，定義未知待估參數的估計即此線性模型的最小二乘估計（ ? ） _ n ；然後將所得估計（ ? ） _ n代入原半參數回歸模型中，用一般的非參數權函數方法定義未知函數g （ ? ）的估計（ -
Let be a sequence of independent and identically distributed random variables ， with mean and variance. while the distribution function is unknown ， and is large ， then is a normal approximation distribution
3設相互獨立的隨機變量服從同一分佈，已知均值為，方差為.單分佈函數未知，當充分大時，近似服從正態分佈 -
In this thesis single input - single output and multiple input - multiple output stochastic systems are discussed respectively. innovations are introduced to reconstruct the original minimum variance control problem of stochastic system, which is unsolvable by means of dynamic programming. so it can be converted into multiple single - step control problems, in which kalman filter is used to estimate unknown system parameters
本文分別針對單輸入單輸出和多輸入多輸出的隨機系統進行了研究，通過引入系統的新息對原不可解的動態規劃問題進行重構，將系統參數隨機變化的最小方差控制問題轉化成為多個基於新息的單步控制問題。
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