Research news on Stochastic processes

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 time or space. It encompasses foundational theory for Markov processes, martingales, Lévy processes, Gaussian processes, and point processes, with emphasis on properties such as stationarity, ergodicity, and mixing. Research addresses limit theorems, stochastic calculus, stochastic differential equations, and probabilistic potential theory. Applications span quantitative finance, statistical physics, queuing theory, population dynamics, signal processing, and machine learning, where stochastic processes provide rigorous frameworks for uncertainty quantification and temporal or spatial dependence.

Why averages fail for bacteria in the open ocean

How can bacteria that forage on organic particles survive in vast ocean regions where such particles are extremely sparse? A new study by researchers from ETH Zurich and Queen Mary University of London shows that variability ...

Finding information in the randomness of living matter

When describing collective properties of macroscopic physical systems, microscopic fluctuations are typically averaged out, leaving a description of the typical behavior of the systems. While this simplification has its advantages, ...

Randomness reveals hidden order in the plant world

In the intricate architecture of plant tissues, beauty often emerges from chaos, according to new research from Cornell researchers. Findings from a recent study show how randomness and growth together create the striking ...

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