Abstract: Traditionally power is generated from power stations to load centers in one direction. Due to variable load from Electrical Vehicles (EV’s) and variable generation like Distributed ...

Bernstein polynomial estimators employ weighted sums of Beta basis functions to approximate unknown probability density functions on compact intervals. By representing the target density as a convex ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

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 ...

This paper presents an application of swarm intelligence to a hundred-year-old problem in mathematics, the Hausdorff moment problem (HMP), where one reconstructs a density function from its moments.

Life is uncertain. None of us know what is going to happen. We know little of what has happened in the past or is happening now outside our immediate experience. Uncertainty has been called the ...

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.

Abstract: While probability distribution functions are crucial for simulating random processes, research on these functions and their features is required. However, studies have demonstrated that in ...