Abstract Machine learning systems based on minimizing average error have been shown to
perform inconsistently across notable subsets of the data, which is not exposed by a low …

Bayesian inference requires specification of a single, precise prior distribution, whereas
frequentist inference only accommodates a vacuous prior. Since virtually every real-world …

Hazards in underground coal mines create an unsafe working environment for workers and
equipment. This paper aims to analyze the safety risk in underground coal mining using an …

This study proposes a method for developing equipment lifespan estimators that combine
physical information and numerical data, both of which may be incomplete. Physical …

Many geosciences data are imprecise due to various limitations and uncertainties in the
measuring process. In other situations, collocated measurements of variables from the …

Existing remote sensing data classification methods cannot achieve the sharing of remote
sensing image spectrum, leading to poor fusion and classification of remote sensing data …

This study proposes a methodology for developing deterioration models to estimate the
remaining lifetime of a system using physics-informed learning. The approach consists of …

Learning the deterioration of a battery from charge and discharge data is associated with
different non-random uncertainties. A specific methodology is developed, capable of …

Modeling human ratings data subject to raters' decision uncertainty is an attractive problem
in applied statistics. In view of the complex interplay between emotion and decision making …

Abstract Machine learning has proven extremely useful in many applications in recent years.
However, a lot of these success stories stem from evaluating the algorithms on data very …