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With k-Nearest Neighbor (kNN) density estimation, density is estimated by computing the volume required to include the k nearest neighbors of the current sample [ 55- 57]: (8) p θ → = 1 N k ρ k θ → d v d In this equation ρ k represents the distance to the k t h nearest neighbor, d the number of dimensions and v d the volume of the unit ball in R d.
The source geometry is estimated by computing the structural index from previously determined depth and location.
The separability is estimated by computing the Pearson correlation indices between pairs of variables.
The accuracy of the equivalent source model is estimated by computing the acoustic spectrum at a far-field observer.
The energy supplied to the magnetosphere by the solar wind is estimated by computing integrals of Akasofu's epsilon parameter determined from WIND satellite measurements.
The curvature is estimated by computing the reciprocal of the radius of an osculating circle.
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Parameters were estimated by computing the maximum likelihood estimation of the parameters without any approximation of the model (that is, no linearization) using the stochastic approximation expectation maximization algorithm combined with a Markov chain Monte Carlo procedure.
The level of genetic variation among populations was estimated by computing FST values using the estimator of Weir and Cockerham (1984).
Backgrounds are estimated by computing total B Band counts per chip, minus the sum of B Band source counts (src_cnts_aper_b) from all sources on the chip.
Reliability for the math motivation scale was estimated by computing the Cronbach's Alpha (α = .91).91
Reliability for the mathematics motivation scale was estimated by computing the Cronbach's Alpha (α = .83).83
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