Noun:

সহভেদাংক,

covariance শব্দের বাংলা অর্থ এর উদাহরণ:

ত্রুটিসমূহ পরস্পর সম্পর্কহীন, তার মানে, ত্রুটিসমূহের ভেদাংক-সহভেদাংক ম্যাট্রিক্স (variance-covariance matrix) একটি কর্ণ ম্যাট্রিক্স, এবং প্রতিটি ।

কোন বিপরীত ম্যাট্রিক্সের কর্ণ থেকে পাওয়া যায় (অজানা রাশির পশ্চাদ্বর্তী-সহভেদাংক (posterior-covariance) ম্যাট্রিক্স) ।

covariance's Usage Examples:

and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square.

In probability theory and statistics, covariance is a measure of the joint variability of two random variables.

eigenvectors of the data's covariance matrix.

Thus, the principal components are often computed by eigendecomposition of the data covariance matrix or singular.

Analysis of covariance (ANCOVA) is a general linear model which blends ANOVA and regression.

is known as weak-sense stationarity, wide-sense stationarity (WSS), or covariance stationarity.

covariance of two variables, divided by the product of their standard deviations; thus it is essentially a normalised measurement of the covariance,.

Lorentz covariance, a related concept, is a property of the underlying spacetime manifold.

Lorentz covariance has two distinct, but closely.

The sample mean (or "empirical mean") and the sample covariance are statistics computed from a sample of data on one or more random variables.

Subtracting the mean before multiplication yields the cross-covariance between times t 1 {\displaystyle t_{1}} and t 2 {\displaystyle t_{2}}.

In theoretical physics, general covariance, also known as diffeomorphism covariance or general invariance, consists of the invariance of the form of physical.

In multilinear algebra and tensor analysis, covariance and contravariance describe how the quantitative description of certain geometric or physical entities.

" Though regardless of Gaussianity, if the process and measurement covariances are known, the Kalman filter is the best possible linear estimator in.

standard deviation, the second central moment of a distribution, and the covariance of the random variable with itself, and it is often represented by σ 2.

Subtracting the mean before multiplication yields the auto-covariance function between times t 1 {\displaystyle t_{1}} and t 2 {\displaystyle.

w_{i}/V_{1}=1/N} , then the weighted mean and covariance reduce to the unweighted sample mean and covariance above.

done by comparing the actual covariance matrices representing the relationships between variables and the estimated covariance matrices of the best fitting.

\operatorname {E} [X_{k}])^{\textbf {T}},} and k × k {\displaystyle k\times k} covariance matrix Σ i , j = E ⁡ [ ( X i − μ i ) ( X j − μ j ) ] = Cov ⁡ [ X i , X.

canonical variates analysis, is a way of inferring information from cross-covariance matrices.

Synonyms:

variance;

Antonyms:

changelessness; sameness;