Applied Probability and Stochastic Processes: Tutorial 02

Related lectures (31)

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Covers the analysis of random walks and their return probabilities in multiple dimensions.

Covers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.

Covers stochastic processes in continuous time, including probability functions and stock processes.

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Explores discrete random variables, mass functions, and distribution functions in probability and statistics.

Covers free fields and probabilistic functions, focusing on random variables and their properties.

Covers the characterization of stochastic processes, focusing on their mathematical foundations and real-world applications.

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

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