The extreme or unanchored discrepancy is the geometric discrepancy of point sets in the d-dimensional unit cube with respect to the set system of axis-parallel boxes. For 2 ≤ p < ∞ we provide upper bounds for the average Lp-extreme discrepancy. With these bounds we are able to derive upper bounds for the inverse of the L∞-extreme discrepancy with optimal dependence on the dimension d and explic...

Tractability properties of various notions of discrepancy have been intensively studied in the last decade. In this paper we consider the so-called weighted star discrepancy which was introduced by Sloan and Woźniakowski. We show that under a very mild condition on the weights one can obtain tractability with s-exponent zero (s is the dimension of the point set). In the case of product weights ...

Similarly to β-adic van der Corput sequences, abstract van der Corput sequences can be defined by abstract numeration systems. Under some assumptions, these sequences are low discrepancy sequences. The discrepancy function is computed explicitly, and the bounded remainder sets of the form [0, y) are characterized.

Aim: To evaluate the influence of cobalt-chromium (Co-Cr) coping fabrication methods and ceramic application on marginal internal fit metal-ceramic crowns. Methods: Co-Cr copings for crowns were prepared by lost wax casting or CAD-CAM machining sintered blocks. The was analyzed using silicone replica technique at four assessment points: gap (MG), axial wall (AW), axio-occlusal (AO) angle, centr...

In this paper, we study a class of two sample test statistics based on inter-point distances in the high dimensional and low/medium size setting. Our include well-known energy distance maximum mean discrepancy with Gaussian Laplacian kernels, critical values are obtained via permutations. We show that all these tests inconsistent when distributions correspond to same marginal but differ other a...

Due to the scarcity of high-quality labeled speech emotion data, it is natural apply transfer learning recognition. However, learning-based recognition becomes more challenging because complexity and ambiguity emotion. Domain adaptation based on maximum mean discrepancy considers marginal alignment source domain target domain, but not pay regard class prior distribution in both domains, which r...

Recently, it has been shown that the shape of a marginal distribution can be more accurately and efficiently captured using set low discrepancy sequence (LDS) points compared to standard grid points. This suggests use LDS could improve approximation posterior distributions produced by grid-based Bayesian methods such as Integrated Nested Laplace Approximation (INLA). However, obtaining posterio...

Abstract In machinery fault diagnosis, labeled data are always difficult or even impossible to obtain. Transfer learning can leverage related diagnosis knowledge from fully source domain enhance the performance in sparsely unlabeled target domain, which has been widely used for cross diagnosis. However, existing methods focus on either marginal distribution adaptation (MDA) conditional (CDA). p...

This work proposes a distribution-free stochastic model updating framework to calibrate the joint probabilistic distribution of multivariate correlated parameters. In this framework, marginal distributions are defined as staircase density functions and correlation structure is described by Gaussian copula function. The first four moments coefficients updated an approximate Bayesian computation,...