Abstract: This article investigates a novel robust Kalman filter (RKF) by incorporating kernel density estimation (KDE) in the Kalman filtering framework to address the disturbance of measurement ...

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Density estimation is a fundamental component in statistical analysis, aiming to infer the probability distribution of a random variable from a finite sample without imposing restrictive parametric ...

This study introduces two-dimensional (2D) Kernel Density Estimation (KDE) plots as a novel tool for visualising Training Intensity Distribution (TID) in biathlon. The goal was to assess how KDE plots ...

Set create_data and do_plotting to True and press play. Initiation is at the bottom of the script. Requires numpy, matplotlib, and scipy. adk_estimator: Contains the functions that does the adaptive ...

Abstract: With the rapid development of monitoring system and sensor technology, a large amount of data streams that continuously arrive with intensive data traffic have been generated in various ...

The quantum low-density parity check (QLDPC) codes, the “holy grail” of quantum error correction research and development for 30 years, have a breakthrough ...

ABSTRACT: In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning ...

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In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, ...