Sentence examples for maximizing over a from inspiring English sources

We show that redescending M-estimator can be designed by applying a global minimax criterion to locally robust estimators, namely maximizing over a class of densities the minimum variance sensitivity over a class of estimators.

In other words, for an exact game, the additive value functions in the XOS representation of the game can be taken to be those corresponding to the elements of the core (if we allow maximizing over a potentially infinite set of additive value functions).

Since (B.1) is continuously differentiable over the new feasible region given by (B.3) and (B.4), we can now apply Theorem 3. Thus, we can devise an iterative algorithm to maximize the function, by maximizing over for fixed ( for fixed ) and then maximizing for fixed ( for fixed ).

Our aim is to minimize the variance of the estimation error to maximize over and.

Since each mobile and static node performs their detection problems independent of each other, maximizing over all possible movement plans for will maximize the sum at time.

In effect, one infers the face pose and landmarks by maximizing over all mixtures and over all possible shapes given the patch HOGs.

We first maximize (over a neighborhood of the fitted response function) and then average (with respect to a prior on the parameters) the sum (over the design space) of the mean squared errors of the predictions.

By adopting the relaxation in (3), we expand the feasible set, therefore, the objective function in (3) is maximized over a larger set than in (1), thus v qp A i, b i, c i i = 0 N ≤ v sdp A i, b i, c i i = 0 N. (4).

In this case, the total amount of uplink real-time capacities in the network is maximized over a restricted parameter set, which is determined by the specified upper bounds on mean nrtPS packet delays and by the specified range of loads.

For the Pareto product to apply, we have the prerequisite that algebras A and B both maximize over a total order.

For the M-step, the expected log likelihood is maximized over a given parameter by taking the derivative as explained above.