Stochastic processes provide a rigorous framework for modelling systems that evolve over time under uncertainty, while extremal theory offers the tools for understanding the behaviour of rare ...

M. H. A. Davis, Piecewise-Deterministic Markov Processes: A General Class of Non-Diffusion Stochastic Models, Journal of the Royal Statistical Society. Series B (Methodological), Vol. 46, No. 3 (1984) ...

A probability distribution governing the evolution of a stochastic process has infinitely many Bayesian representations of the form $\mu =\int_ {\Theta}\mu _ {\theta }d\lambda (\theta)$. Among these, ...

Theory of stochastic processes Stochastic processes are at the center of probability theory, both from a theoretical and an applied viewpoint. Stochastic processes have applications in many ...

A second course in stochastic processes and applications to insurance. Markov chains (discrete and continuous time), processes with jumps; Brownian motion and diffusions; Martingales; stochastic ...

The dynamic world of the very small is the focus of a new textbook, “Stochastic Thermodynamics: An Introduction”, co-authored by Luca Peliti, professor emeritus of statistical mechanics at the ...

435IEMS 435: Stochastic Simulation VIEW ALL COURSE TIMES AND SESSIONS Prerequisites Statistics, and real analysis at the undergraduate engineering or mathematics level; graduate level probability and ...

IEMS 460-2 : (OPNS 516) Stochastic Processes II VIEW ALL COURSE TIMES AND SESSIONS Description This course provides doctoral students the foundations of applied probability and stochastic modeling.

A second course in stochastic processes and applications to insurance. Markov chains (discrete and continuous time), processes with jumps; Brownian motion and diffusions; Martingales; stochastic ...