covariance of estimates
1Covariance intersection — is an algorithm for combining two or more estimates of state variables in a Kalman filter when the correlation between them is unknown.[1][2][3] Specification Items of information a and b are known and are to be fused into information item c. We… …
2Covariance — This article is about the measure of linear relation between random variables. For other uses, see Covariance (disambiguation). In probability theory and statistics, covariance is a measure of how much two variables change together. Variance is a …
3Covariance and correlation — Main articles: covariance, correlation. In probability theory and statistics, the mathematical descriptions of covariance and correlation are very similar.[1][2] Both describe the degree of similarity between two random variables or sets of… …
4Estimation of covariance matrices — In statistics, sometimes the covariance matrix of a multivariate random variable is not known but has to be estimated. Estimation of covariance matrices then deals with the question of how to approximate the actual covariance matrix on the basis… …
5Eddy covariance — The eddy covariance (eddy correlation, eddy flux) technique is a prime atmospheric flux measurement technique to measure and calculate vertical turbulent fluxes within atmospheric boundary layers. It is a statistical method used in meteorology… …
6Sample mean and sample covariance — are statistics computed from a collection of data, thought of as being random.ample mean and covarianceGiven a random sample extstyle mathbf{x} {1},ldots,mathbf{x} {N} from an extstyle n dimensional random variable extstyle mathbf{X} (i.e.,… …
7Kalman filter — Roles of the variables in the Kalman filter. (Larger image here) In statistics, the Kalman filter is a mathematical method named after Rudolf E. Kálmán. Its purpose is to use measurements observed over time, containing noise (random variations)… …
8Linear regression — Example of simple linear regression, which has one independent variable In statistics, linear regression is an approach to modeling the relationship between a scalar variable y and one or more explanatory variables denoted X. The case of one… …
9Maximum likelihood — In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum likelihood estimation provides estimates for the model s… …
10Kriging — is a group of geostatistical techniques to interpolate the value of a random field (e.g., the elevation, z , of the landscape as a function of the geographic location) at an unobserved location from observations of its value at nearby locations.… …
