Random Processes and Monte Carlo Simulation

Related lectures (32)

Page 3 of 4

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Covers the properties of the symmetric random walk in stochastic processes.

Covers the essentials of probability, algebras, and conditional probability, including the Borel o-algebra and Poisson processes.

Covers examples of ergodicity in continuous-time stochastic processes, illustrating concepts such as ergodicity and random processes.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers mathematical tools for communication systems and data science, including information theory and signal processing.

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

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Explores the construction of Brownian motion with continuous trajectories and the dimension of its zero set.