Stochastic Models for Communications: Markov Chains and Random Variables

Related lectures (31)

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Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Explores independent and identically distributed random variables, convergence, and distribution properties.

Covers the Central Limit Theorem and its application to random variables, proving convergence to a normal distribution.

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Introduces the Metropolis-Hastings algorithm for efficient simulation of random variables with given probabilities.

Explores moment-generating functions, linear combinations, and normality of random variables.

Covers the theory of Brownian motion, diffusion, and random walks, with a focus on Einstein's theory for one-dimensional motion.

Explores continuous random variables, cdf, pdf, convolution, and sum of dice.

Explores exponential random variables, memorylessness, and applications in stochastic processes and Markov chains.

Covers the modes of convergence of random variables and the Central Limit Theorem, discussing implications and approximations.