Mixture models have been widely used for data clustering. However, commonly used mixture models are generally of a parametric form (e.g., mixture of Gaussian distributions or GMM), which significantly limits their capacity in fitting diverse multidimensional data distributions encountered in practice. We propose a non-parametric mixture model (NMM) for data clustering in order to detect cluster...

Abstract In model-based control schemes, the importance of performing robust stability and performance analysis is partly due to the presence of uncertainty. This uncertainty can be modeled as structured or unstructured, though this paper deals primarily with structured uncertainty, specifically parametric uncertainty. Parametric uncertainty can be manifested in the form of parametric tolerance...

For providing a benchmark data for numerical codes of parametric rolling prediction, a model experiment of a post-Panamax C11 class containership whose hull form is slightly modified from its original but opened for public was conducted and significant parametric rolling in irregular head waves was recorded. The existing numerical model developed by the authors for parametric rolling in regular...

This paper discusses the use of fractional exponential models (Robinson (1990), Beran (1994)) to model the spectral density f(x) of a covariance stationary process when f(x) may be decomposed as f(x) = x ?2d f (x), where f (x) is bounded and bounded away from zero. A form of log-periodogram regression technique is presented both in the parametric context (i.e. f (x) is a nite order exponential ...

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. Machine learning : a probabilistic perspective / Kevin P. Murphy. p. cm. — (Adaptive computation and machine learning series) Includes bibliographical references a...

The generalized parametric structural schematic diagram, the generalized structural-parametric model, and the generalized matrix transfer function of an electromagnetoelastic actuator with output parameters displacements are determined by solving the wave equation with the Laplace transform, using the equation of the electromagnetolasticity in the general form, the boundary conditions on the lo...

We compare parametric and nonparametric estimation methods in the context of PBPK modeling using simulation studies. We implement a Monte Carlo Markov Chain simulation technique in the parametric method, and a functional analytical approach to estimate the probability distribution function directly in the nonparametric method. The simulation results suggest an advantage for the parametric metho...

The spectral correlation of a chaotic system with spin 1/2 is universally described by the GSE (Gaussian Symplectic Ensemble) of random matrices in the semiclassical limit. In semiclassical theory, the spectral form factor is expressed in terms of the periodic orbits and the spin state is simulated by the uniform distribution on a sphere. In this paper, instead of the uniform distribution, we i...