Lectures related to Stochastic

Covers the outcomes of a virtual seminar on stochastic analysis.

Delves into structuring sound, exploring parameters like pitch, rhythm, and dynamics, and discussing techniques like repetition and gradual change.

Explores the M/G/infinity queue model for systems with infinite capacity and general service times.

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

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

Explores the probabilistic description of turbulence, focusing on reproducibility and statistics in turbulent signals and deterministic chaos.

Covers stochastic simulation and variance reduction techniques, focusing on Courra variate and auxiliary distribution generation.

Explores uncertainty quantification using Quasi Monte Carlo methods and discrepancy measures for integral approximation and volume estimation.

Covers examples of continuous-time stochastic processes in communication systems.

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

Covers deterministic and stochastic rainfall models in water resources engineering, including generation, calibration, and spatially explicit models.

Covers linear-quadratic stochastic differential games on directed chain networks and open-loop Nash equilibria.

Explores stochastic simulation, rare events, and the Crude Monte Carlo method, emphasizing the importance of thresholds and closed-form expressions.

Introduces Markov chains and Metropolis Hastings algorithm in stochastic simulation.

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

Covers the stochastic simulations course, G/G/1 queueing model, computational finance, statistics, physics, and Bayesian inference.

Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.

Covers stochastic models for communication systems, including concepts like stochastic processes and Markov chains.

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

Covers the basics of L-Systems, explores various examples, discusses stochastic variations, and extends to 3D modeling.