Sentence examples for mean location estimate from inspiring English sources

The proposed non-intrusive interference mitigation approach, which we call Border-UA, intentionally tries to overestimate I R (i,p) for all i ∈ S T and p ∈ S P by calculating the estimated interference inside or at the edge of the UA surrounding p's mean location estimate, and the UA size of p is chosen with the parameter ΩUC (p).

The collection of particles from all spanning trees represents our final output, from which we can easily extract any parameter that we need (e.g, mean value for location estimate).

The estimate's components are: (1) the estimated mean location μ ^ s of a binding event, (2) the estimated mean total number ν ^ s of right (or left) tags within R s due to the binding event, and (3) the estimated uniform background intensity ρ ^ s of right (or left) tags within R s.

What a cool graphic! a, Daily mean position estimates (circles) and annual median deployment locations (white squares) of all tagged species.

The root mean square error (RMSE) of the location estimate, which is defined as (sqrt {E[(hat {mathbf {x}}-mathbf {x})^{2}]}) in general, is used as a performance measure in this paper.

The methodology uses the concept of winsorization to provide robust estimates of the mean (location) and S.D. (scale) iteratively, yielding a robust set of data.

However, they can only provide a coarse-grained estimate of each node's location, which means that they are only suitable for applications requiring an approximate location estimate.

We used mean location and mean depth in the analysis.

A simple approach is to use a single measurement from each location in 2002 to estimate mean location-specific exposures in 2002 (M1).

Through Monte Carlo simulations, this nonparametric estimator is found to provide more accurate and precise mean parameters and QTL location estimates than the parametric AR(1) form for the covariance model, especially when the underlying covariance structure of the data is significantly different from the assumed model.

The Root Mean Square Error (RMSE) of the location estimates is chosen as the performance criteria.