Centrality measures allow to identify important nodes in networked systems. An open question in network theory is the empirical observation that a node’s centrality—whose computation requires ...

Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...

A courseware module that covers the fundamental concepts in probability theory and their implications in data science. Topics include probability, random variables, and Bayes' Theorem.

A long-standing challenge in statistical mechanics has been to quantify the nonequilibrium nature of active systems such as swarming bacteria, schooling fish, and flocking birds. The entropy ...

Quantum annealing (QA) can be competitive to classical algorithms in optimizing continuous-variable functions when running on appropriate hardware, show researchers from Tokyo Tech. By comparing the ...

Abstract: When assessing product life, the survival analysis is generally conducted in a time or usage domain. In certain instances, it is beneficial to investigate the impact of joint variables on ...

To accurately evaluate the influence of the uncertainty and correlation of photovoltaic (PV) output and load on the running state of power system, a probabilistic optimal power flow (POPF) calculation ...

Abstract: We present two novel methods for the generation of Fox's H-function distributed random variables (RVs), which have been recently used to model fading in various wireless communication ...

ABSTRACT: This paper derives a mathematical description of the complex stretch processor’s response to bandlimited Gaussian noise having arbitrary center frequency and bandwidth. The description of ...