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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.
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 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.
Reliability for the math motivation scale was estimated by computing the Cronbach's Alpha (α = .91).91
The curvature is estimated by computing the reciprocal of the radius of an osculating circle.
Reliability for the mathematics motivation scale was estimated by computing the Cronbach's Alpha (α = .83).83
The direction of the electric current is estimated by computing the flow velocity vectors.
Therefore, the nonnegative loading matrix can be estimated by computing the joint diagonalizer.
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Significance was estimated by computing H0 (the null hypothesis) probability using the Monte Carlo method (999 replicates).
The activation moment of the remaining blocks in the datapath is then estimated by computing their distance to the reference block based on the latency and the throughput of the blocks preceding them.
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