Sentence examples for minimum variance estimate from inspiring English sources

This paper investigates conditions under which the state estimate given by this algorithm converges asymptotically to the first order minimum variance estimate given by the extended Kalman filter (EKF).

The authors conducted an investigation of the conditions under which the state estimate given by this algorithm converges asymptotically to the first order minimum variance estimate given by the extended Kalman filter (EKF).

We admit the presence of several cooperating control centers, and we design two distributed methods for them to compute the minimum variance estimate of the state, given the network measurements.

In the assimilation step, the Kalman filter adjusts the electron densities based on the innovation vector and the prior covariance matrix to compute a statistically minimum variance estimate of the electron density.

The EKF makes use of a first-order Taylor approximation of the state transition and thus does not approach the true minimum variance estimate when the linearization error is non-negligible.

The second type of controller predicts the minimum variance estimate of control action using recall process (network inversion) and the control law is derived following a Lyapunov function synthesis approach so that the closed loop system consisting of controller and neural emulator remains stable.

Such estimates are also called minimum variance estimates.

We address the estimation problem by a maximum-likelihood approach, which is known to yield optimal (unbiased and minimum variance) estimates for our problem setting in the case where Y is fully known.

These include minimum variance unbiased estimate (MVUE) and minimum norm solutions, which are very well established estimation techniques [7].

STATA performs poorly for studies with a very low or very high event rate and so by default changes zero frequencies to 0.5 in order to give a minimum variance unbiased estimate.

One of the most widely used tools in estimation theory is the Kalman filter (Kalman 1960), which gives a sequential, unbiased, minimum error variance estimate based upon a linear combination of real time observations and dynamics.