Sentence examples for heuristic estimator from inspiring English sources

Homotopy classes are generated and sorted according to a lower bound heuristic estimator using a method we developed.

The one-sample balance heuristic estimator using the {α i } values in Eq. 51 is more efficient than the one-sample balance heuristic estimator with equal count of sampling when the {α i } values are decreasing with costs {c i }.

Observe that (hat {F}) is the balance heuristic estimator defined in Eq. 12 where the N i ∝σi,eq.

However, the value of objective function in Eq. (5) can be a suitable heuristic estimator in this task.

It is guided by a heuristic estimator based on regression-match graphs, which attempt to characterize the entire subgoal structure of the remaining part of the problem.

In Section 4, we present a multi-sample balance heuristic estimator that is provably better than multi-sample balance heuristic with equal count of samples.

We have obtained, both without and with taking into account the cost of sampling, new balance heuristic estimators that are provably better than balance heuristic with equal count of samples, and new heuristic estimators valid even when the independent techniques are biased.

We improve on their work by clarifying the quasi-optimality rules in Veach's thesis by introducing new balance heuristic estimators that are provably better than balance heuristic with equal count of samples, and by introducing a new heuristic valid when the individual estimators are biased.

Lu et al. [7] propose an improvement to balance heuristic estimators for environment map illumination by using a Taylor's second order approximation of the variance around the equal weights 1/2 to obtain the counts of samples according to the BRDF and the environment map, which is accurate only if the optimal sample numbers are not too far from equal sampling.

The degree of overfitting (shrinkage) in the model will be estimated using the heuristic shrinkage estimator (based on the log likelihood ratio χ statistic for the full model).

Next, we examine a recently introduced non-balance heuristic MIS estimator that is provably better than balance heuristic with equal count of samples, and we improve it both in variance and efficiency.