Stochastic Models for Communications

Related lectures (30)

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Covers elementary distributions like Bernoulli, Binomial, Geometric, Negative Binomial, Poisson, and Multinomial.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

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

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Explores statistical models, families of distributions, transformations, and their applications in probability theory.

Explores modes of convergence in probability and statistics, illustrating concepts with examples and discussing the continuity theorem.

Covers random variables, probability functions, expectations, variances, and joint distributions.

Covers various probability distributions and their applications in real-world scenarios.

Explores joint random variables, conditional density, and independence in probability and statistics.

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