Random Processes and Monte Carlo Simulation

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Related lectures (32)

Page 2 of 4

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Markov Chains: Properties and Expectations

Explores Markov chains' properties, expectations, and recurrence in Poisson processes.

Point Processes: Convergence and Gaussian Processes

Covers point processes, convergence criteria, Laplace functionals, Gaussian processes, covariance functions, and intrinsic stationarity.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Luria-Delbrück Experiment

Explores the implications of the Luria-Delbrück experiment on evolutionary mechanisms and the importance of probabilities in understanding biological data.

Rainfall Models: Deterministic vs Stochastic

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

Monte Carlo methods: single particle diffusion

Covers Monte Carlo methods for simulating single particle diffusion in cementitious materials, including random functions and number generators.

Stochastic Models for Communications: Continuous-Time Stochastic Processes

Covers examples of continuous-time stochastic processes in communications.

Random Variables and Probability Densities

Explores random variables, probability densities, Gaussian distribution, and conditional probabilities in measurement systems.

Markov Chains: Definitions and Transitions

Explains Markov chains, transition matrices, and stationary distributions in random processes.