We begin by reviewing the statistical framework of information theory as applicable to
neuroimaging data analysis. A major factor hindering wider adoption of this framework in …

Entropy is a measure of uncertainty and has been commonly used for various applications,
including probability inferences in hydrology. Copula has been widely used for constructing …

Many atomic descriptors are currently limited by their unfavourable scaling with the number
of chemical elements S eg the length of body-ordered descriptors, such as the SOAP power …

The input variable selection problem has recently garnered much interest in the time series
modeling community, especially within water resources applications, demonstrating that …

Real-world data typically contain a large number of features that are often heterogeneous in
nature, relevance, and also units of measure. When assessing the similarity between data …

The integration and transfer of information from multiple sources to multiple targets is a core
motive of neural systems. The emerging field of partial information decomposition (PID) …

Objective. The human brain tracks the temporal envelope of speech, which contains
essential cues for speech understanding. Linear models are the most common tool to study …

Artificial neural networks (ANNs) have proved to be an efficient alternative to traditional
methods for hydrological modeling. One of the most important steps in the ANN …

The rainfall–runoff relationship is not only nonlinear and complex but also difficult to model.
Artificial neural network (ANN), as a data-driven technique, has gained significant attention …

The problem of optimal data collection to efficiently learn the model parameters of a graphite
nitridation experiment is studied in the context of Bayesian analysis using both synthetic and …