Stochastic Models for Communications: Continuous-Time Stochastic Processes Examples 4-5

Related lectures (32)

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Covers the concept of ergodicity in continuous-time stochastic processes.

Explores martingales in stochastic systems, focusing on formal analysis, termination analysis, and stability verification.

Illustrates ergodicism in continuous-time stochastic processes through examples and calculations.

Explores spatial ergodicity for SPDEs, covering basic formulations, initial data effects, and results on ergodicity and CLT.

Explores classical mean field theory, local interactions, and examples like individual-based SIR models and raindrop formation.

Explores Markov chain convergence, emphasizing invariant distribution, Law of Large Numbers, and mean rewards computation.

Covers stochastic models for communications and the estimation of correlation functions.

Explores continuous-time stochastic processes in communication systems.

Explores the application of randomness in physical models, focusing on Brownian motion and diffusion.

Explores the Fokker-Planck and KPZ equations in stochastic processes and random growth models.