網(wǎng)絡釋義

用法例句

For arbitrary conditional estimable function,the linear conditional minimax estimator under a given quadratic loss function is defined and the unique linear conditional minimax estimator is obtained.

對任一條件可估函數(shù) ,給出了二次損失下線性條件 Minimax估計的定義 ,并得到了唯一的線性條件Minim ax估計 。

For arbitrary estimable function,the unique linear minimax estimator under a given matrix loss function is obtained in the class of homogeneous linear estimators.

考慮方差分量模型,對任一可估函數(shù),在二次損失下得到了線性可估函數(shù)在齊次估計類中的唯一的線性M in-im ax估計。

Let Y be a random n-vector with mean Xβ and covariance matrix σ2V, and S be a linear estimable function, where X, Sβ and V ≥ 0 are known matrics, ∈ Rp and σ ≥ 0 are unknown parameters.

設Y是具有均值Xβ和協(xié)方差陣σ~2V的n維隨機向量,Sβ是線性可估函數(shù),這里X,S和V≥0是已知矩陣,β∈R~p和σ~2>0是未知參數(shù)。

Reinforcement learning function approximation algorithm based on linear average;

基于線性平均的強化學習函數(shù)估計算法

Blind source separation based on optimally selected estimating functions;

基于選優(yōu)估計函數(shù)的盲信號分離

Secondly, based on the semiparametric theory, an estimating function is constructed and the corresponding learning algorithms are proposed.

基于此,采用半?yún)?shù)統(tǒng)計方法構造超定盲信號分離的估計函數(shù),給出相應的學習算法;理論證明了該算法具有等變化性和分離矩陣的非奇異特性,并借助于源信號數(shù)目未知且動態(tài)變化的計算機仿真驗證了其有效性。

Firstly, the semiparametric statistical approach is introduced into the BSS, and an estimating function for the semiparametric statistical approach in BSS is proposed, from which a learning rule is obtained.

將半?yún)?shù)統(tǒng)計模型引入源信號個數(shù)未知的盲分離中,給出了源信號個數(shù)(其值n不大于觀測信號的個數(shù)m)未知,混合矩陣列滿秩時,盲分離半?yún)?shù)統(tǒng)計模型的估計函數(shù),得到了由此估計函數(shù)給出的半?yún)?shù)統(tǒng)計學習算法。

The approach can ensure the minimum actual risk of denoised signals in the view of function estimation,overcoming the drawbacks of application of traditional wavelet-denoising approaches.

根據(jù)統(tǒng)計學習的結構風險最小化原則和VC維理論,給出一種改進的基于VC維的小波消噪方法,使消噪后信號在函數(shù)估計意義下具有最小的實際風險,克服了傳統(tǒng)的小波信號消噪方法的應用缺陷。

THE LINEAR MINLMAX ESTIMATE OF ESTIMABLE FUNCTION IN A GENERAL NORMAL LINEAR MODEL;

一般正態(tài)線性模型中可估函數(shù)的線性Minimax估計

On Minimax Estimators of Estimable Functions in Normal Linear Experiments;

正態(tài)線性模型中可估函數(shù)的極小極大估計(英文)

On Minimax Estimators of Estimable Funetions in Normal Linear Experiments;

正態(tài)線性試驗中可估函數(shù)的最小最大估計

The Minimax Admissibility of the Estimable Function and the Linear Prediction under Quadratic Loss Fuction;

二次損失下可估函數(shù)與線性預測的Minimax可容許性

The Minimax Adimissibility of the Linear Estimator in the Gauss-markov Model under the Quadratic Function;

二次損失下一般Gauss-Markov模型中可估函數(shù)的Minimax可容許性

The admissible linear estimates of the mean matrix on the matrix-normal distribution

矩陣正態(tài)分布均值矩陣的可估函數(shù)的線性估計在線性估計類中的泛容許性

assessment of additive utility function

可加效用函數(shù)的評估

Mean Value on Some Arithmetical Functions and Solvability of the Functional Equations;

一些數(shù)論函數(shù)的均值估計及一類函數(shù)方程的可解性

Bayesina Estimation of Geometric Distribution Parameter under Entropy Loss Function;

熵損失函數(shù)下幾何分布可靠度的Bayes估計

Henstock integrable function and staircase function;

Henstock可積函數(shù)與階梯函數(shù)

Parameter Estimation of Archimedean Copula

Archimedean Copula函數(shù)的參數(shù)估計

Admissibility of Parameter Estimators in Linear Model under Vector Loss Function;

向量損失函數(shù)下線性模型中參數(shù)估計的可容許性

countably subadditive function

可數(shù)次可加性的函數(shù)

All Admissible Estimators of the Function of Mean Matrix in Multivariate Linear Models

多元線性模型中均值矩陣的函數(shù)的所有可容許線性估計

Estimates for Nonparametric Regression Functions with Missing Data

缺失數(shù)據(jù)情形非參數(shù)回歸函數(shù)的估計

Nonparametric estimation of the production function with time-varying elasticity coefficients

時變彈性系數(shù)生產(chǎn)函數(shù)的非參數(shù)估計

Piecewise Function,Integrability and Existence of Primitive Function

分段函數(shù)、函數(shù)的可積性與原函數(shù)存在性

Research on Angular Deveation Estimation of Modulus Function of Grotzsch Domain Function;

關于Grotzsch區(qū)域函數(shù)的模函數(shù)的角偏差估計研究