The discovery of causal relationships from purely observational data is a fundamental
problem in science. The most elementary form of such a causal discovery problem is to …

Complex systems are increasingly being viewed as distributed information processing
systems, particularly in the domains of computational neuroscience, bioinformatics, and …

Data-driven analysis methods, such as the information content of a fitness sequence,
characterize a discrete fitness landscape by quantifying its smoothness, ruggedness, or …

We propose and analyse estimators for statistical functionals of one or moredistributions
under nonparametric assumptions. Our estimators are derived from the von Mises …

In geophysical surveys or experiments, recorded data are used to constrain properties of the
planetary subsurface, oceans, atmosphere or cryosphere. How the experimental data are …

To improve the fault identification accuracy of rolling bearing and effectively analyze the fault
severity, a novel rolling bearing fault diagnosis and severity analysis method based on the …

Abstract I introduce Forecastable Component Analysis (ForeCA), a novel dimension
reduction technique for temporally dependent signals. Based on a new forecastability …

Nonparametric estimation of mutual information is used in a wide range of scientific
problems to quantify dependence between variables. The k-nearest neighbor (knn) methods …

Information theory is an outstanding framework for measuring uncertainty, dependence, and
relevance in data and systems. It has several desirable properties for real-world …

Джон фон Нейман. Предисловие к книге: Мартин Н., Ингленд Дж. Математическая
теория энтропии.–М.: Мир, 1988, с. 18. Роль математического моделирования в …