Sentence examples for probability density estimation from inspiring English sources

The basis of this novel strategy is inspired from the k-Nearest Neighbours (kNN) approach and adaptive kernel methods for probability density estimation (kernel density estimators, KDE) [11].

We first validated the probability density estimation using Fourier inversion.

This article focuses on the histogram and kernel probability density estimation methods for statistical analysis of biomedical data.

The coherent point drift method[44] formulated the alignment of two point sets as a probability density estimation problem.

We apply this approach to the sludge process and compare the results to the probability density estimation obtained with Monte-Carlo simulations.

We design a coupled shape prior to model the transformation between a related pair of surfaces, using a nonparametric joint probability density estimation.

For each set of events, we included three probability density estimations for comparison.

It is used for tasks such as clustering, mode-seeking, probability density estimations, and tracking [17, 18].

Two linkage estimators, the minimum convex polygon (MCP), and the incremental cluster-analysis polygon (ICP) and two probability density estimators, the fixed kernel density estimation with reference smoothing factor (KDE with href), and with least squares cross-validation to calculate smoothing factor (KDE LSCV with hlscv).

In this work, a non-parametric method for probability density function estimation based on kernel density estimation [7] is used.

Moreover, in the framework of the probability density function estimation, the nearest neighbour method provides density estimates depending on the Euclidean distance to the k-th nearest data point [19].