Stochastic processes form the backbone of modern probability theory, describing systems that evolve randomly over time or space. They are instrumental in areas ranging from statistical physics to ...

This course provides doctoral students the foundations of applied probability and stochastic modeling. The first part of the course covers basic concepts in probability, such as the Borel Cantelli ...

The beauty of Statistics is that if you can take a large enough group of people, you can predict really well what the outcome will be overall Our research works across the fields of probability, ...

Let X(t) be a homogeneous and continuous stochastic process with independent increments. The subject of this paper is to characterize the stable process by two identically distributed stochastic ...

This is a preview. Log in through your library . Abstract In this paper we discuss a counter system whose output is a stochastic point process such that the time intervals between pairs of successive ...

Applications range from medical imaging to autonomous vehicle technology. Learn data manipulation techniques to improve signal or image fidelity. Understand the theory of probability and stochastic ...

Editor's note: As the following article is a chapter (Chapter 8) from David Koenig's book, Practical Control Engineering: Guide for Engineers, Managers, and Practitioners (MATLAB Examples) (McGraw ...

Students must have completed or currently enrolled in a course in the equivalency group containing MATH 310-2 or MATH 311-2. Prerequisite: Students must have completed or currently enrolled in a ...