Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

Abstract: Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology and science. In this work, we investigate the fusion of ...

A continuous random variable can assume an uncountably infinite number of values within a given range, differentiating it fundamentally from a discrete random variable which can only take on distinct, ...

This research provides density functions and descriptive statistics for the distance between points for basic shapes in Cartesian space. Both Euclidean and Rectilinear Distances are determined for ...

Department of Computer Science, Metropolitan College, Boston University, Boston, MA, United States On the other hand, using MAD offers a direct measure of deviation and is more resilient to outliers.

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 ...

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 ...