Nonparametric Kernel Density Estimation And Its Computational Aspects
Download Nonparametric Kernel Density Estimation And Its Computational Aspects PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Nonparametric Kernel Density Estimation And Its Computational Aspects book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Nonparametric Kernel Density Estimation and Its Computational Aspects
This book describes computational problems related to kernel density estimation (KDE) – one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.
Motion Planning for Autonomous Vehicles in Partially Observable Environments
Author: Taş, Ömer Şahin
language: en
Publisher: KIT Scientific Publishing
Release Date: 2023-10-23
This work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that ensure safety. The resulting problem is solved in real-time, in two distinct ways: first, with nonlinear optimization, and secondly, by framing it as a partially observable Markov decision process and approximating the solution with sampling.
Monitoring Multimode Continuous Processes
Author: Marcos Quiñones-Grueiro
language: en
Publisher: Springer Nature
Release Date: 2020-08-04
This book examines recent methods for data-driven fault diagnosis of multimode continuous processes. It formalizes, generalizes, and systematically presents the main concepts, and approaches required to design fault diagnosis methods for multimode continuous processes. The book provides both theoretical and practical tools to help readers address the fault diagnosis problem by drawing data-driven methods from at least three different areas: statistics, unsupervised, and supervised learning.