In this article, we present a novel approach to quantitative evaluation of a model for parsing web pages as visual images, intended to provide improvements for users with assistive needs (cognitive or visual deficits, enabling decluttering or zooming and ...

We consider the two-armed bandit problem in the application to batch data processing if there are two alternative processing methods with different a priori unknown efficiencies, and income is understood as successfully processed data. It is ...

A quantile scalar-on-image regression model is developed to comprehensively study the relationship between cognitive decline and various clinical covariates and imaging factors. As a motivating example, the high-dimensional brain imaging data ...

This competitive environment is rapidly driving technological modernization. Sophisticated cyber security attacks are expanding exponentially, inflicting reputation damage and financial and economic loss. Since security investments may take time ...

Errors in formulating the system physics model, due to inability to rigorously account for nonlinear behavior, boundary conditions, and multi-component and multi-physics interactions result in discrepancies between model predictions of system ...

• Estimating errors in dynamics systems using Bayesian state estimation.
• Propagating model errors to an untested configuration.
• Non-intrusive methodology for use with black-box models.

In multi-class classification, the output of a probabilistic classifier is a probability distribution of the classes. In this work, we focus on a statistical assessment of the reliability of probabilistic classifiers for multi-class problems. Our ...${}^{\mathrm{}}$

We discuss stochastic optimization problems under distributional ambiguity. The distributional uncertainty is captured by considering an entire family of distributions. Because we assume the existence of data, we can consider confidence regions ...

In most compressive sensing algorithms, such as L1-optimization and greedy family techniques, the only a priori information utilized in the reconstruction procedure is the sparsity information. Meanwhile, there exists another family of techniques ...

The use of Cauchy Markov random field priors in statistical inverse problems can potentially lead to posterior distributions which are non-Gaussian, high-dimensional, multimodal and heavy-tailed. In order to use such priors successfully, ...

Baseline assumptions play a crucial role in conducting consistent quantitative policy assessments for dynamic Computable General Equilibrium (CGE) models. Two essential factors that influence the determination of the baselines are the data sources ...

In the area of remote sensing image processing, accurate segmentation of high-resolution remote sensing images in real time remains a challenging problem and numerous approaches have been developed for the problem. This paper proposes a new ...

The problem of handwritten digit recognition has seen various developments in the recent times, especially in neural network domain. The methods based on neural network work quite effectively for the seen classes of data by providing deterministic ...

Phase-field modeling of fracture has gained popularity within the last decade due to the flexibility of the related computational framework in simulating three-dimensional arbitrarily complicated fracture processes. However, the numerical ...

The dependency effect to extreme value distributions (EVDs) using the frequentist and Bayesian approaches have been used to analyse the extremes of annual and daily maximum wind speed at Port Elizabeth, South Africa. In the frequentist approach, ...

The choice of the parameter value for regularized inverse problems is critical to the results and remains a topic of interest. This article explores a criterion for selecting a good parameter value by maximizing the probability of the ...

Spline functions have proved to be very useful in statistics, in particular, to estimate the nonparametric regression. Many different smoothing parameter selectors for the smoothing spline are proposed in the literature such as cross-validation (CV), ...

Complex product development increasingly entails creation and sharing of design knowledge in a collaborative and integrated working environment. In this context, it has become a central issue to address the multifaceted feature of design ...

Structural equation modeling (SEM) is a powerful, comprehensive, and flexible multivariate statistical method for modeling relationships between observed and latent variables. However, in genetic association analysis, frequentist approaches to ...

This paper is concerned with the estimation problem of a periodic autoregressive model with closed skew-normal innovations. The closed skew-normal (CSN) distribution has some useful properties similar to those of the Gaussian distribution. Maximum ...

The research is aimed at coping with the inherent computational intensity of Bayesian multi-objective optimization algorithms. We propose the implementation which is based on the rectangular partition of the feasible region and circumvents much of ...