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We present an improved analysis of the Euler-Maruyama discretization of the Langevin diffusion. Our analysis does not require global contractivity, and yields polynomial dependence on the time horizon. Compared to existing approaches, we make an additional ...
Train wheel flats are formed when wheels slip on rails. Crucial for passenger comfort and the safe operation of train systems, early detection and quantification of wheel-flat severity without interrupting railway operations is a desirable and challenging ...
The computational prediction of crystal structures has emerged as an useful alternative to expensive and often cumbersome experiments. We propose an approach to the prediction of crystal structures and polymorphism based on reproducing the crystallization ...
Evans et al. [1] proved the subadditivity of the mutual information in the broadcasting on tree model with binary vertex labels and symmetric edge channels. They raised the question of whether such subadditivity extends to loopy graphs in some appropriate ...
Due to conservative design models and safe construction practices, infrastructure often has significant yet-unknown reserve capacity that greatly exceeds code requirements. Reserve-capacity assessments lead to better asset-management decisions through eith ...
This chapter makes the first attempt to quantify the amount of discriminatory information in finger vein biometric characteristics in terms of Relative Entropy (RE) calculated on genuine and impostor comparison scores using a Nearest Neighbour (NN) estimat ...
We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme ...
Many control schemes require significant tuning effort to achieve desired performance targets, causing a need for general tools that can perform controller tuning in an automated fashion. This paper represents a continuation of the previous work in which a ...
A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these nominal distributions are themselves estimated from data, which makes them s ...
The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood ...