covariance

In practice, the class means and covariances are not known.

The variance is the covariance of the data with itself.

These covariances are an example of an "evolutionary arms race."

A covariance model has seven types of states and production rules (Table 1).

Let us start with the definition of the sample distance covariance.

The magnitude of the covariance is not easy to interpret.

The population value of distance covariance can be defined along the same lines.

Such a process exists because the given covariance is positive definite.

If the variables are correlated, then covariance must be taken into account.

However, f and h cannot be applied to the covariance directly.

If x and y are independent, then their covariance is zero.

This covariance matrix is a useful tool in many different areas.

There are several ways to define the two covariance matrices.

How does one arrive at the required covariance then?

I may not understand it yet, but covariance and outliers are familiar terms.

See the covariance matrix sections on the discussion page for more information.

In this sense covariance is a linear gauge of dependence.

Since the covariance only depends on distances between points, it is stationary.

One can also find there similar formulas for covariance.

Very similar algorithms can be used to compute the covariance.

For a large number of samples, the average converges to the true covariance.

We also offer the full covariances of our estimated parameters to any interested parties.

These sigma points completely capture the mean and covariance of the system state.

The result is a filter which more accurately captures the true mean and covariance.

In addition, the procedure is robust to the covariance structure among variables.