The field of data science is expanding and changing as new technologies and methods crop up for data scientists. Those familiar with point processes know that they map collections of data points, or ...

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 ...

We develop a unified stochastic framework for a broad class of nonequilibrium systems and apply it to turbulence and cosmic structure formation. The evolution of spectra and distributions in these ...

Stochastic processes as a research area focuses on the mathematical modeling and analysis of systems that evolve randomly over time, typically formalized as families of random variables indexed by ...

Increasing evidence has emerged for non-random spatial distributions of microbes, but knowledge of the processes that cause variation in microbial assemblage among ecosystems is lacking. For instance, ...

Stochastic processes are at the center of probability theory, both from a theoretical and an applied viewpoint. Stochastic processes have applications in many disciplines such as physics, computer ...

Simulation research derives new methods for the design, analysis, and optimization of simulation experiments. Research on stochastic models develops and analyzes models of systems with random behavior ...

Leo Tolstoy’s War and Peace is, of course, as much a meditation upon history and human agency as it is a novel that explores the lives of several interconnected aristocratic Russian families against ...

Our research focus here employs probabilistic and analytic methods to explore complex random systems. At its core, the methods developed and utilized include a wide array of tools from stochastic ...