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 ...

Markov chains and processes, random walks, stationary, independent increments, and Poisson processes. Ergodicity. Examples (e.g., diffusion, queuing theory, etc.).

Natural processes, such as rain falling, the motion of groups of insects or birds, or the random movement of smoke particles in air may be described as stochastic.

In this seminar we will develop techniques for the analysis of stochastic processes. Talks are distributed in dependence on previous knowledge with the aim of extending the mathematical foundations in ...

Throughout, we will be applying some of the theoretic results to the analysis of queues. Students are expected to have some background in probability (such as IEMS 202) and stochastic processes; no ...

A stochastic process is a mathematical model for phenomena unfolding dynamically and unpredictably over time. This module studies two classes of stochastic process particularly relevant to financial ...

Students working in simulation prepare by undertaking rigorous training in stochastic processes, statistics, and optimization. Students also obtain a solid grounding in the application domain of ...

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 ...

Natural processes, such as rain falling, the motion of groups of insects or birds, or the random movement of smoke particles in air may be described as stochastic.