Sentence examples for by a kernel density from inspiring English sources

These distributions are estimated by a kernel density estimator or Bayesian semiparametric estimator.

It extends the radial plot by a kernel density estimate plot, histogram or dot plot and contains elements that link both plot types.

The idea consists of treating each particle as a sample drawn from the pool of molecules that it represents; this way, the actual location of a tracked particle is seen as a sample drawn from the density function of the location of molecules represented by that given particle, rigorously represented by a kernel density function.

Calculating the probability from a set of samples is typically done by using a kernel density estimation (KDE) procedure [22], also known as a Parzen window method [23].

Our algorithm, T-KDE, identifies binding site locations by combining a kernel density estimator (KDE) with a binary range tree.

We also analyzed the distribution of markers along the chromosomes, by using a kernel density estimation to calculate optimal window size (bandwidth) for dividing the genome into blocks, in which we counted the number of genes.

Using the subset of records assigned to each of the 20 pure parental populations, we calculated their respective climatic areas by using a kernel density function [ 62] (bivariate normal kernel, 95% density, 500 × 500 pixel grid); this method of estimating area in the PCA was preferable to a minimum convex polygon, which would have included large areas devoid of any sampling points.

The factors included in the CT analysis were 1) household (i.e., type of house, head of household occupation/education) and biologic (i.e., density of domestic animals around households, collected in a house-to-house survey in July 2006 and estimated by using a kernel density function [ArcGIS, ESRI]) (6 ) and 2) results of PCR analyses (Table 1).

minAS obtains a cutoff value that separates the first component from the rest by firstly estimating the density function of the statistic with a Kernel Density Estimator and by then computing the point where the first local minimum is reached.

To illustrate differences in the distributions of outbreaks in cattle and sheep, the 2 most affected livestock species, a kernel density estimation was performed by using the Spatial Analyst Extension in ArcGIS.

This result, strictly considered, disqualifies K as a kernel density function as kernel density volumes are unitary by definition.