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Covariation model. Harold Kelley 's covariation model (1967, 1971, 1972, 1973) [1] is an attribution theory in which people make causal inferences to explain why other people and ourselves behave in a certain way. It is concerned with both social perception and self-perception (Kelley, 1973). The covariation principle states that, "an effect is ...
Covariance. The sign of the covariance of two random variables X and Y. Covariance in probability theory and statistics is a measure of the joint variability of two random variables. [1] The sign of the covariance, therefore, shows the tendency in the linear relationship between the variables. If greater values of one variable mainly correspond ...
correlation. so that. where E is the expected value operator. Notably, correlation is dimensionless while covariance is in units obtained by multiplying the units of the two variables. If Y always takes on the same values as X, we have the covariance of a variable with itself (i.e. ), which is called the variance and is more commonly denoted as ...
Ensemble Kalman filter. The ensemble Kalman filter ( EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the ...
Analysis of covariance ( ANCOVA) is a general linear model that blends ANOVA and regression. ANCOVA evaluates whether the means of a dependent variable (DV) are equal across levels of one or more categorical independent variables (IV) and across one or more continuous variables. For example, the categorical variable (s) might describe treatment ...
The same C(x, y) is called the autocovariance function in two instances: in time series (to denote exactly the same concept except that x and y refer to locations in time rather than in space), and in multivariate random fields (to refer to the covariance of a variable with itself, as opposed to the cross covariance between two different variables at different locations, Cov(Z(x 1), Y(x 2))).
Although it is now understood that Einstein's response to Kretschmann was mistaken (subsequent papers showed that such a theory would still be usable), another argument can be made in favor of general covariance: it is a natural way to express the equivalence principle, i.e., the equivalence in the description of a free-falling observer and an ...
The use of a fixed measure is necessary for rigor to ensure that a sequence of updates does not cause the filtered covariance to increase. Advantages. According to a recent survey paper and, the covariance intersection has the following advantages: The identification and computation of the cross covariances are completely avoided.